Electrified railways are becoming a popular transport medium and these consume a large amount of electrical energy.Environmental concerns demand reduction in energy use and peak power demand of railway systems.Further...Electrified railways are becoming a popular transport medium and these consume a large amount of electrical energy.Environmental concerns demand reduction in energy use and peak power demand of railway systems.Furthermore,high transmission losses in DC railway systems make local storage of energy an increasingly attractive option.An optimisation framework based on genetic algorithms is developed to optimise a DC electric rail network in terms of a comprehensive set of decision variables including storage size,charge/discharge power limits,timetable and train driving style/trajectory to maximise benefits of energy storage in reducing railway peak power and energy consumption.Experimental results for the considered real-world networks show a reduction of energy consumption in the range 15%–30%depending on the train driving style,and reduced power peaks.展开更多
Accelerated development of battery technologies heightens an interest in co-locating battery energy storage systems (BESSs) with renewable power plants for stacking of multiple revenue streams such as frequency respon...Accelerated development of battery technologies heightens an interest in co-locating battery energy storage systems (BESSs) with renewable power plants for stacking of multiple revenue streams such as frequency response services to AC grids. Frequency response market reforms in the UK introduce new end-state services and require evaluating techno-economic feasibility of co-location projects in new circumstances. This paper develops a BESS optimisation method to optimize capacity and operating strategy of a co-located BESS for providing latest Dynamic Containment (DC) services based on the UK perspective. BESS optimisation method simulates BESS delivering DC responses and following operational baselines for state of energy (SoE) restoration, as well as, coordinating with its co-located power plant. Then net present value of BESS co-location project is estimated from power flows across the system and maximised to suggest optimal BESS capacity, target energy footroom and/or headroom levels for baseline estimation, and possible SoE ranges suitable for energy interchange with its co-located power plant. BESS optimisation method is tested based on a particular transmission-level wind farm in the UK and discussed alongside operation and profitability of a BESS co-location project under frequency response market reforms.展开更多
文摘Electrified railways are becoming a popular transport medium and these consume a large amount of electrical energy.Environmental concerns demand reduction in energy use and peak power demand of railway systems.Furthermore,high transmission losses in DC railway systems make local storage of energy an increasingly attractive option.An optimisation framework based on genetic algorithms is developed to optimise a DC electric rail network in terms of a comprehensive set of decision variables including storage size,charge/discharge power limits,timetable and train driving style/trajectory to maximise benefits of energy storage in reducing railway peak power and energy consumption.Experimental results for the considered real-world networks show a reduction of energy consumption in the range 15%–30%depending on the train driving style,and reduced power peaks.
基金supported by the research programme of the Electrical Infrastructure Research Hub in collaboration with the Offshore Renewable Energy Catapult.
文摘Accelerated development of battery technologies heightens an interest in co-locating battery energy storage systems (BESSs) with renewable power plants for stacking of multiple revenue streams such as frequency response services to AC grids. Frequency response market reforms in the UK introduce new end-state services and require evaluating techno-economic feasibility of co-location projects in new circumstances. This paper develops a BESS optimisation method to optimize capacity and operating strategy of a co-located BESS for providing latest Dynamic Containment (DC) services based on the UK perspective. BESS optimisation method simulates BESS delivering DC responses and following operational baselines for state of energy (SoE) restoration, as well as, coordinating with its co-located power plant. Then net present value of BESS co-location project is estimated from power flows across the system and maximised to suggest optimal BESS capacity, target energy footroom and/or headroom levels for baseline estimation, and possible SoE ranges suitable for energy interchange with its co-located power plant. BESS optimisation method is tested based on a particular transmission-level wind farm in the UK and discussed alongside operation and profitability of a BESS co-location project under frequency response market reforms.
