Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity...Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid.The design of any DSM system using a wireless network must consider the wireless link impairments,which is missing in existing literature.In this paper,we propose a DSM system using a Real-Time Pricing(RTP)mechanism and a wireless Neighborhood Area Network(NAN)with data transfer uncertainty.A Zigbee-based Internet of Things(IoT)model is considered for the communication infrastructure of the NAN.A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link.The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users,decision-makers,and energy providers.A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices.Simulation results indicate that the proposed system benefits users and energy providers.Furthermore,experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN,which can substantially impact the performance of the proposed DSM system.Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price,user welfare,and provider welfare.展开更多
In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a sma...In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.展开更多
With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage co...With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.展开更多
Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufactur...Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.展开更多
To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources(DSR)based on the Stackelberg game theory.First,the concept of t...To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources(DSR)based on the Stackelberg game theory.First,the concept of the generalized DSR is analyzed and flexibility models for various DSR are constructed.Second,owing to the characteristics of small capacity but large-scale,an outer approximation is proposed to describe the aggregate flexibility of DSR.Then,the optimal flexibility dispatch model of DSR based on the Stackelberg game is established and a decentralized solution algorithm is designed to obtain the Stackelberg equilibrium.Finally,the actual data are utilized for the case study and the results show that,compared to the traditional centralized optimization method,the proposed optimal flexibility dispatch method can not only reduce the net load variability of the DSR aggregator but is beneficial for all DSR owners,which is more suitable for practical applications.展开更多
Microgrids are a type of restricted power distribution systems in which electricity is generated,transmitted,and distributed within a small geographic region.They are used to ensure that renewable energy sources are u...Microgrids are a type of restricted power distribution systems in which electricity is generated,transmitted,and distributed within a small geographic region.They are used to ensure that renewable energy sources are used to their full potential.Microgrids provide further benefits,such as lowering transmission losses and the expenses associated with them.This research compares and contrasts the aims of economic dispatch,emission dispatch,fractional programing based combined economic emission dispatch,and environmental restricted economic dispatch(ECED).A low-voltage microgrid system is investigated for three different scenarios.As a study optimization tool,an innovative,resilient,and strong hybrid swarm-intelligence optimization algorithm is utilised,which is based on combining the properties of the traditional grey-wolf optimiser,sine-cosine algorithm,and crow search algorithm.The employment of a time-of-use energy mar-ket pricing approach instead of a fixed pricing plan resulted in a 15%decrease in gen-eration costs throughout the course of the research.When ECED was assessed with a 15%-20%demand side management based restructured load demand model for the microgrid system,the generation costs were reduced even further.展开更多
Nowadays renewable energy has become a trend for energy production but its variable nature has made balancing of demand and supply of the power grid difficult. Dynamic demand management using smart appliances is propo...Nowadays renewable energy has become a trend for energy production but its variable nature has made balancing of demand and supply of the power grid difficult. Dynamic demand management using smart appliances is proposed to serve as a way that part of the regulation burden of balancing demand and supply is shifted to the demand side. However, if all appliances respond to the same frequency deviation, they may start to synchronize, causing large power overshoots and instability of the power grid. Therefore, the idea of implementing randomness into the frequency control of the appliances is proposed and this is what we call a stochastic approach. Simulators are built from scratch to model both scenarios. The effect of synchronization is analyzed and the parameters that can affect the synchronization are investigated. It has been found that the larger the contribution from the smart appliances to the power grid, the easier and faster the synchronization takes place. The stochastic approach solves the problem of synchronization and averages out the large power overshoot. However, the overall performance of stochastic operations is unacceptable due to the randomness in the operation though the mean and variance are as expected. More advanced feedback policies and schemes may be designed to achieve a better performance.展开更多
The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefor...The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems （TABS） based on multiple linear regression （AMLR） presented in this paper enables the application of demand side management （DSM） strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.展开更多
The work presents a demand side response（DSR） model,which assists electricity consumers to proactively mitigate peak demand on electrical networks in Eastern and Southern Australia. A low-cost technical arrangement,...The work presents a demand side response（DSR） model,which assists electricity consumers to proactively mitigate peak demand on electrical networks in Eastern and Southern Australia. A low-cost technical arrangement,which is made of Internet relay,a router,solid state switches,and the suitable software,is used to control electricity demand at user＇s premises. The model allows shifting loads from peak to off-peak periods in order to reduce peaks,which helps to moderate the national electrical demand. The model can be concurrently used to accommodate the utilization of renewable energy sources and the introduction of electric vehicles. The results present possible savings on the electrical energy consumption in the designated regions.展开更多
The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. H...The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.展开更多
The introduction of new kinds of energy mixes to the electricity grid is a challenging environmental task for present and future generations as they fight the pollution and global warming issues associated with urbani...The introduction of new kinds of energy mixes to the electricity grid is a challenging environmental task for present and future generations as they fight the pollution and global warming issues associated with urbanization. Individual appliances and whole buildings that continuously incorporate local intelligence which originates from the new technologies of Internet of Things are the new infrastructure that this integration is based on. Smart Electricity Grids are becoming more intensively integrated with tertiary building energy management systems and distributed energy generators such as wind and solar. This new smart network type harnesses the loT （lnternet of Things） principles by generating a new network made of active elements combined with the necessary control and distributed coordination mechanisms. This new self-organized overlay network of connected DER （distributed energy resources） allows for the seamless management and control of the active grid as well as the efficient coordination and exploration of single and aggregated technical prosumer potential （generation and consumption） to participate in energy balancing and other distributed grid related services, applying energy management strategies based on control and predict of the DERs behavior for facing demand side management issues.展开更多
Recent estimates state that the European Union is on course to achieve only half of the 20% energy consumption reduction target by 2020. As the first governmental stakeholders involved in the implementation of energy ...Recent estimates state that the European Union is on course to achieve only half of the 20% energy consumption reduction target by 2020. As the first governmental stakeholders involved in the implementation of energy saving initiatives, municipalities play a strategic role in the energy planning process. This paper focuses on establishment of an energy planning methodology for small municipalities with numbers of inhabitants in range of 1,000-10,000 which often face common problems associated with low efficient district heat supply systems and decreasing energy consumption in buildings. Particular attention is paid to DSM （demand side management） activities. DSM scheme includes legislative and financial flows with small investments from municipality side. Based on increased information and motivation it promotes reduction of energy consumption in all kinds of buildings. Practical experience has shown that application of DSM measures allows achieving 20% energy savings in municipal buildings during the first year.展开更多
Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Tran...Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Transmission and distribution and land issues for new generation plants, which force the utilities to search for another alternatives without any additional constraints on customers comfort level or quality of delivered product. De can be defined as the selection, planning, and implementation of measures intended to have an influence on the demand or customer-side of the electric meter, either caused directly or stimulated indirectly by the utility. DSM programs are peak clipping, Valley filling, Load shifting, Load building, energy conservation and flexible load shape. The main Target of this paper is to show the relation between DSM and Load Forecasting. Moreover, it highlights on the effect of applying DSM on Forecasted demands and how this affects the planning strategies for utility companies. This target will be clearly illustrated through applying the developed algorithm in this paper on an existing residential compound in Cairo-Egypt.展开更多
This paper analyzes the relationship between the stock of infrastructure and income increases using data from 15 typical countries,including China,and measures the gap between China and npper-middlle-income countries ...This paper analyzes the relationship between the stock of infrastructure and income increases using data from 15 typical countries,including China,and measures the gap between China and npper-middlle-income countries using the Euclidean distance.By constructing a domestic infrastructure investment demand model,this paper provides the basis for determining the growth rates for infrastructure investment demand under the given economic development goals and assessing the rationality of such growth rates.The paper finds that,as the per-capita income level increases, the total infrastructure demand rises but different types of infrastructure stock grow at different paces.Using the 2004 domestic infrastructure level as the benchmark for international comparison,we find it imperative for China to further boost resource infrastructure construction in the future and keep resource infrastructure investment growing at an average annual rate of 15%-24%.The infrastructure investment growth rate should be kept above the nominal GDP growth rate.展开更多
Contraction of resilience on generation side due to the introduction of inflexible renewable energy sources is demanding more elasticity on consumption side. It requires more intelligent systems to be implemented to m...Contraction of resilience on generation side due to the introduction of inflexible renewable energy sources is demanding more elasticity on consumption side. It requires more intelligent systems to be implemented to maintain power balance in the grid and to fulfill the consumer needs. This paper is concerned about the energy balance management of the system using intelligent agent-based architecture. The idea is to limit the peak power of each individual household for different defined time regions of the day according to power production during those time regions. Monte Carlo Simulation (MCS) has been employed to study the behavior of a particular number of households for maintaining the power balance based on proposed technique to limit the peak power for each household and even individual load level. Flexibility of two major loads i.e. heating load (heat storage tank) and electric vehicle load (battery) allows us to shift the peaks on demand side proportionally with the generation in real time. Different parameters related to heating and Electric Vehicle (EV) load e.g. State of Charge (SOC), storage capacities, charging power, daily usage, peak demand hours have been studied and a technique is proposed to mitigate the imbalance of power intelligently.展开更多
In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative mu...In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration.In the initial stage,using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads,including temperature-controlled loads,electric vehicles,and energy storage devices,a novel calculation method for their maximum adjustable capacities is devised.Distinct from conventional methods,this newly developed approach enables more precise and adaptable quantification of the load-adjusting capabilities,thereby enhancing the accuracy and flexibility of demand-side resource management.Subsequently,an SSA-BiLSTM flexible load classification prediction model is established.This model represents an innovative application in the field,effectively combining the advantages of the Sparrow Search Algorithm(SSA)and the Bidirectional Long-Short-Term Memory(BiLSTM)neural network.Furthermore,a parallel Markov chain is introduced to evaluate the switching state transfer probability of flexible loads accurately.This integration allows for a more refined determination of the maximum response capacity range of the flexible load aggregator,significantly improving the precision of capacity assessment compared to existing methods.Finally,in consonance with the intra-day scheduling plan,a newly developed diffuse filling algorithm is implemented to control the activation times of flexible loads precisely,thus achieving real-time dynamic demand response.Through in-depth case analysis and comprehensive comparative studies,the effectiveness of the proposed method is convincingly validated.With its innovative techniques and enhanced performance,it is demonstrated that this method has the potential to substantially enhance the utilization efficiency of demand-side resources in power systems,providing a novel and effective solution for optimizing power grid operation and demand-side management.展开更多
The integration of substantial renewable energy and controllable resources disrupts the supply-demand balance in distribution grids.Secure operations are dependent on the participation of user-side resources in demand...The integration of substantial renewable energy and controllable resources disrupts the supply-demand balance in distribution grids.Secure operations are dependent on the participation of user-side resources in demand response at both the day-ahead and intraday levels.Current studies typically overlook the spatial--temporal variations and coordination between these timescales,leading to significant day-ahead optimization errors,high intraday costs,and slow convergence.To address these challenges,we developed a multiagent,multitimescale aggregated regulation method for spatial--temporal coordinated demand response of user-side resources.Firstly,we established a framework considering the spatial--temporal coordinated characteristics of user-side resources with the objective to min-imize the total regulation cost and weighted sum of distribution grid losses.The optimization problem was then solved for two different timescales:day-ahead and intraday.For the day-ahead timescale,we developed an improved particle swarm optimization(IPSO)algo-rithm that dynamically adjusts the number of particles based on intraday outcomes to optimize the regulation strategies.For the intraday timescale,we developed an improved alternating direction method of multipliers(IADMM)algorithm that distributes tasks across edge distribution stations,dynamically adjusting penalty factors by using historical day-ahead data to synchronize the regulations and enhance precision.The simulation results indicate that this method can fully achieve multitimescale spatial--temporal coordinated aggregated reg-ulation between day-ahead and intraday,effectively reduce the total regulation cost and distribution grid losses,and enhance smart grid resilience.展开更多
Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainabili...Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.展开更多
As an essential characteristic of the smart grid,energy demand users are being transformed from passive roles to active decision-makers.To analyze their decision-making behaviors,game theory has been widely applied on...As an essential characteristic of the smart grid,energy demand users are being transformed from passive roles to active decision-makers.To analyze their decision-making behaviors,game theory has been widely applied on the demand side.This paper focuses on the classification and in-depth analysis of recent studies that propose game-theoretic approaches for decision optimi-zation of multiple demand users.This analysis classifies scenarios into various game participant categories,in-cluding distributed energy prosumers,small-and mid-dle-sized users,and large energy consumers.The in-depth analysis of each scenario,covering non-cooperative game,cooperative game,Stackelberg game,Bayesian game,and evolutionary game,is conducted by analyzing market operation mechanisms,model assumptions/formulations,and solution methods.Based on a comprehensive review of such studies,it is concluded that game-theoretic appli-cations on the demand side can benefit both the grid and the users,e.g.,reductions in the peak-to-average ratios and energy costs of the users.The prospects for the ap-plications of game theory on the demand side are dis-cussed,including application scenarios and methodologies.The overview presented in this paper is expected to sup-port researchers in comprehending typical game-theoretic concepts,keeping with the latest research developments,and identifying new and innovative appli-cations for the energy demand side.Index Terms—Energy demand side,game theory,Game-theoretic application,demand response,deci-sion-making behavior.展开更多
文摘Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid.The design of any DSM system using a wireless network must consider the wireless link impairments,which is missing in existing literature.In this paper,we propose a DSM system using a Real-Time Pricing(RTP)mechanism and a wireless Neighborhood Area Network(NAN)with data transfer uncertainty.A Zigbee-based Internet of Things(IoT)model is considered for the communication infrastructure of the NAN.A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link.The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users,decision-makers,and energy providers.A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices.Simulation results indicate that the proposed system benefits users and energy providers.Furthermore,experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN,which can substantially impact the performance of the proposed DSM system.Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price,user welfare,and provider welfare.
文摘In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.
基金supported by Science and Technology Project of SGCC(SGSW0000FZGHBJS2200070)。
文摘With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.
基金Supported by National Natural Science Foundation of China(Grant No.61272428)PhD Programs Foundation of Ministry of Education of China(Grant No.20120002110067)
文摘Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.
基金supported by Science and Technology Project of State Grid Hebei Electric Power Company(SGHE0000DKJS2000228)
文摘To promote the utilization of renewable energy,such as photovoltaics,this paper proposes an optimal flexibility dispatch method for demand-side resources(DSR)based on the Stackelberg game theory.First,the concept of the generalized DSR is analyzed and flexibility models for various DSR are constructed.Second,owing to the characteristics of small capacity but large-scale,an outer approximation is proposed to describe the aggregate flexibility of DSR.Then,the optimal flexibility dispatch model of DSR based on the Stackelberg game is established and a decentralized solution algorithm is designed to obtain the Stackelberg equilibrium.Finally,the actual data are utilized for the case study and the results show that,compared to the traditional centralized optimization method,the proposed optimal flexibility dispatch method can not only reduce the net load variability of the DSR aggregator but is beneficial for all DSR owners,which is more suitable for practical applications.
文摘Microgrids are a type of restricted power distribution systems in which electricity is generated,transmitted,and distributed within a small geographic region.They are used to ensure that renewable energy sources are used to their full potential.Microgrids provide further benefits,such as lowering transmission losses and the expenses associated with them.This research compares and contrasts the aims of economic dispatch,emission dispatch,fractional programing based combined economic emission dispatch,and environmental restricted economic dispatch(ECED).A low-voltage microgrid system is investigated for three different scenarios.As a study optimization tool,an innovative,resilient,and strong hybrid swarm-intelligence optimization algorithm is utilised,which is based on combining the properties of the traditional grey-wolf optimiser,sine-cosine algorithm,and crow search algorithm.The employment of a time-of-use energy mar-ket pricing approach instead of a fixed pricing plan resulted in a 15%decrease in gen-eration costs throughout the course of the research.When ECED was assessed with a 15%-20%demand side management based restructured load demand model for the microgrid system,the generation costs were reduced even further.
文摘Nowadays renewable energy has become a trend for energy production but its variable nature has made balancing of demand and supply of the power grid difficult. Dynamic demand management using smart appliances is proposed to serve as a way that part of the regulation burden of balancing demand and supply is shifted to the demand side. However, if all appliances respond to the same frequency deviation, they may start to synchronize, causing large power overshoots and instability of the power grid. Therefore, the idea of implementing randomness into the frequency control of the appliances is proposed and this is what we call a stochastic approach. Simulators are built from scratch to model both scenarios. The effect of synchronization is analyzed and the parameters that can affect the synchronization are investigated. It has been found that the larger the contribution from the smart appliances to the power grid, the easier and faster the synchronization takes place. The stochastic approach solves the problem of synchronization and averages out the large power overshoot. However, the overall performance of stochastic operations is unacceptable due to the randomness in the operation though the mean and variance are as expected. More advanced feedback policies and schemes may be designed to achieve a better performance.
基金supported by the Ministry of Science,Research and Arts(MWK)of Baden-Württemberg,Germany,as part of a Ph.D.scholarship
文摘The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems （TABS） based on multiple linear regression （AMLR） presented in this paper enables the application of demand side management （DSM） strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.
基金supported by Directorate General of Higher Education Department of National Education, the Indonesian Government and the State Polytechnic of Ujung Pandang, Makassar, Indonesia, and the Australia Power Institute (API)
文摘The work presents a demand side response（DSR） model,which assists electricity consumers to proactively mitigate peak demand on electrical networks in Eastern and Southern Australia. A low-cost technical arrangement,which is made of Internet relay,a router,solid state switches,and the suitable software,is used to control electricity demand at user＇s premises. The model allows shifting loads from peak to off-peak periods in order to reduce peaks,which helps to moderate the national electrical demand. The model can be concurrently used to accommodate the utilization of renewable energy sources and the introduction of electric vehicles. The results present possible savings on the electrical energy consumption in the designated regions.
基金supported in part by the National Science Foundation of China (61973247, 61673315, 62173268)the Key Research and Development Program of Shaanxi (2022GY-033)+2 种基金the Nationa Postdoctoral Innovative Talents Support Program of China (BX20200272)the Key Program of the National Natural Science Foundation of China (61833015)the Fundamental Research Funds for the Central Universities (xzy022021050)。
文摘The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.
文摘The introduction of new kinds of energy mixes to the electricity grid is a challenging environmental task for present and future generations as they fight the pollution and global warming issues associated with urbanization. Individual appliances and whole buildings that continuously incorporate local intelligence which originates from the new technologies of Internet of Things are the new infrastructure that this integration is based on. Smart Electricity Grids are becoming more intensively integrated with tertiary building energy management systems and distributed energy generators such as wind and solar. This new smart network type harnesses the loT （lnternet of Things） principles by generating a new network made of active elements combined with the necessary control and distributed coordination mechanisms. This new self-organized overlay network of connected DER （distributed energy resources） allows for the seamless management and control of the active grid as well as the efficient coordination and exploration of single and aggregated technical prosumer potential （generation and consumption） to participate in energy balancing and other distributed grid related services, applying energy management strategies based on control and predict of the DERs behavior for facing demand side management issues.
文摘Recent estimates state that the European Union is on course to achieve only half of the 20% energy consumption reduction target by 2020. As the first governmental stakeholders involved in the implementation of energy saving initiatives, municipalities play a strategic role in the energy planning process. This paper focuses on establishment of an energy planning methodology for small municipalities with numbers of inhabitants in range of 1,000-10,000 which often face common problems associated with low efficient district heat supply systems and decreasing energy consumption in buildings. Particular attention is paid to DSM （demand side management） activities. DSM scheme includes legislative and financial flows with small investments from municipality side. Based on increased information and motivation it promotes reduction of energy consumption in all kinds of buildings. Practical experience has shown that application of DSM measures allows achieving 20% energy savings in municipal buildings during the first year.
文摘Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Transmission and distribution and land issues for new generation plants, which force the utilities to search for another alternatives without any additional constraints on customers comfort level or quality of delivered product. De can be defined as the selection, planning, and implementation of measures intended to have an influence on the demand or customer-side of the electric meter, either caused directly or stimulated indirectly by the utility. DSM programs are peak clipping, Valley filling, Load shifting, Load building, energy conservation and flexible load shape. The main Target of this paper is to show the relation between DSM and Load Forecasting. Moreover, it highlights on the effect of applying DSM on Forecasted demands and how this affects the planning strategies for utility companies. This target will be clearly illustrated through applying the developed algorithm in this paper on an existing residential compound in Cairo-Egypt.
文摘This paper analyzes the relationship between the stock of infrastructure and income increases using data from 15 typical countries,including China,and measures the gap between China and npper-middlle-income countries using the Euclidean distance.By constructing a domestic infrastructure investment demand model,this paper provides the basis for determining the growth rates for infrastructure investment demand under the given economic development goals and assessing the rationality of such growth rates.The paper finds that,as the per-capita income level increases, the total infrastructure demand rises but different types of infrastructure stock grow at different paces.Using the 2004 domestic infrastructure level as the benchmark for international comparison,we find it imperative for China to further boost resource infrastructure construction in the future and keep resource infrastructure investment growing at an average annual rate of 15%-24%.The infrastructure investment growth rate should be kept above the nominal GDP growth rate.
文摘Contraction of resilience on generation side due to the introduction of inflexible renewable energy sources is demanding more elasticity on consumption side. It requires more intelligent systems to be implemented to maintain power balance in the grid and to fulfill the consumer needs. This paper is concerned about the energy balance management of the system using intelligent agent-based architecture. The idea is to limit the peak power of each individual household for different defined time regions of the day according to power production during those time regions. Monte Carlo Simulation (MCS) has been employed to study the behavior of a particular number of households for maintaining the power balance based on proposed technique to limit the peak power for each household and even individual load level. Flexibility of two major loads i.e. heating load (heat storage tank) and electric vehicle load (battery) allows us to shift the peaks on demand side proportionally with the generation in real time. Different parameters related to heating and Electric Vehicle (EV) load e.g. State of Charge (SOC), storage capacities, charging power, daily usage, peak demand hours have been studied and a technique is proposed to mitigate the imbalance of power intelligently.
基金the Science and Technology Project of State Grid Shanxi Electric Power Co.,Ltd.,with the project number 52051L240001.
文摘In contemporary power systems,delving into the flexible regulation potential of demand-side resources is of paramount significance for the efficient operation of power grids.This research puts forward an innovative multivariate flexible load aggregation control approach that takes dynamic demand response into full consideration.In the initial stage,using generalized time-domain aggregation modelling for a wide array of heterogeneous flexible loads,including temperature-controlled loads,electric vehicles,and energy storage devices,a novel calculation method for their maximum adjustable capacities is devised.Distinct from conventional methods,this newly developed approach enables more precise and adaptable quantification of the load-adjusting capabilities,thereby enhancing the accuracy and flexibility of demand-side resource management.Subsequently,an SSA-BiLSTM flexible load classification prediction model is established.This model represents an innovative application in the field,effectively combining the advantages of the Sparrow Search Algorithm(SSA)and the Bidirectional Long-Short-Term Memory(BiLSTM)neural network.Furthermore,a parallel Markov chain is introduced to evaluate the switching state transfer probability of flexible loads accurately.This integration allows for a more refined determination of the maximum response capacity range of the flexible load aggregator,significantly improving the precision of capacity assessment compared to existing methods.Finally,in consonance with the intra-day scheduling plan,a newly developed diffuse filling algorithm is implemented to control the activation times of flexible loads precisely,thus achieving real-time dynamic demand response.Through in-depth case analysis and comprehensive comparative studies,the effectiveness of the proposed method is convincingly validated.With its innovative techniques and enhanced performance,it is demonstrated that this method has the potential to substantially enhance the utilization efficiency of demand-side resources in power systems,providing a novel and effective solution for optimizing power grid operation and demand-side management.
基金supported by Science and Technology Program of China Southern Power Grid Corporation under grant number 036000KK52222004(GDKJXM20222117)National Key R&D Program of China for International S&T Cooperation Projects(2019YFE0118700).
文摘The integration of substantial renewable energy and controllable resources disrupts the supply-demand balance in distribution grids.Secure operations are dependent on the participation of user-side resources in demand response at both the day-ahead and intraday levels.Current studies typically overlook the spatial--temporal variations and coordination between these timescales,leading to significant day-ahead optimization errors,high intraday costs,and slow convergence.To address these challenges,we developed a multiagent,multitimescale aggregated regulation method for spatial--temporal coordinated demand response of user-side resources.Firstly,we established a framework considering the spatial--temporal coordinated characteristics of user-side resources with the objective to min-imize the total regulation cost and weighted sum of distribution grid losses.The optimization problem was then solved for two different timescales:day-ahead and intraday.For the day-ahead timescale,we developed an improved particle swarm optimization(IPSO)algo-rithm that dynamically adjusts the number of particles based on intraday outcomes to optimize the regulation strategies.For the intraday timescale,we developed an improved alternating direction method of multipliers(IADMM)algorithm that distributes tasks across edge distribution stations,dynamically adjusting penalty factors by using historical day-ahead data to synchronize the regulations and enhance precision.The simulation results indicate that this method can fully achieve multitimescale spatial--temporal coordinated aggregated reg-ulation between day-ahead and intraday,effectively reduce the total regulation cost and distribution grid losses,and enhance smart grid resilience.
基金supported by National Key Research and Development Program(2024YFE0115600).
文摘Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.
基金supported by the National Natural Science Foundation of China(No.52107100)the Basic Science(Natural Science)Research Pro-ject of Jiangsu Higher Education Institutions(No.23KJB470020).
文摘As an essential characteristic of the smart grid,energy demand users are being transformed from passive roles to active decision-makers.To analyze their decision-making behaviors,game theory has been widely applied on the demand side.This paper focuses on the classification and in-depth analysis of recent studies that propose game-theoretic approaches for decision optimi-zation of multiple demand users.This analysis classifies scenarios into various game participant categories,in-cluding distributed energy prosumers,small-and mid-dle-sized users,and large energy consumers.The in-depth analysis of each scenario,covering non-cooperative game,cooperative game,Stackelberg game,Bayesian game,and evolutionary game,is conducted by analyzing market operation mechanisms,model assumptions/formulations,and solution methods.Based on a comprehensive review of such studies,it is concluded that game-theoretic appli-cations on the demand side can benefit both the grid and the users,e.g.,reductions in the peak-to-average ratios and energy costs of the users.The prospects for the ap-plications of game theory on the demand side are dis-cussed,including application scenarios and methodologies.The overview presented in this paper is expected to sup-port researchers in comprehending typical game-theoretic concepts,keeping with the latest research developments,and identifying new and innovative appli-cations for the energy demand side.Index Terms—Energy demand side,game theory,Game-theoretic application,demand response,deci-sion-making behavior.
