Abstract
This paper presents linear programming (LP) formulations for short-term energy time-shift operational scheduling with energy storage systems (ESSs) in power grids. In particular, it is shown that the conventional nonlinear formulations for electric bill minimization, peak shaving, and load leveling can be formulated in the LP framework. New variables for the peak and off-peak values are introduced in peak shaving and load leveling model, and the historical peak value for demand charge are considered in the electric bill minimization model. The LP formulations simplify computation while maintaining the accuracy for including linear technical constraints of ESSs, such as the state-of-charge, charging/discharging efficiency, output power range, and energy limit considering the life cycle of ESS. Proposed LP formulations have been implemented and verified in practical power systems and a large-scale industrial customer using historical data.
Highlights
Energy storage systems (ESSs), both mechanical types, such as pumped-hydro energy storage (PHES), compressed-air energy storage (CAES) and flywheels, and battery types, such as Li-ion, NaS and lead-acid, are given opportunities to enter into power systems to supplement conventional generators as they show technological advances and price competitiveness
energy storage systems (ESSs) can be expressed in a form that the energy arbitrage model is included in the unit commitment problem by a system operator, and it can be effectively formulated as mixed integer linear programming (MILP) [9,10]
This paper presented linear programming models for short-term optimal operational scheduling of ESSs as the energy time-shifting application in a power system
Summary
Energy storage systems (ESSs), both mechanical types, such as pumped-hydro energy storage (PHES), compressed-air energy storage (CAES) and flywheels, and battery types, such as Li-ion, NaS and lead-acid, are given opportunities to enter into power systems to supplement conventional generators as they show technological advances and price competitiveness. In [4,5,6,7,8], the energy arbitrage of ESS is tested for power systems with renewable resources These linear formulated energy arbitrage models of ESSs can be flexibly applied in a transaction model with renewables considering uncertainty [4], operation problems of microgrid with other DERs [5,6,7], and the optimal size determination models of ESS considering the installed renewable resources [8]. ESS can be expressed in a form that the energy arbitrage model is included in the unit commitment problem by a system (or market) operator, and it can be effectively formulated as mixed integer linear programming (MILP) [9,10]. The electric bill minimization problem in the customer side is formulated with nonlinear model considering both demand charge and energy charge, where dynamic programming, Markov decision processes, and particle swarm optimization are used in [14,15,16,17], respectively
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