A Two-Step Stochastic Linear Programming Approach for Microgrid Resources and Energy Storage Management with Real-Time Pricing Program Using Salp Swarm Optimization Algorithm
Subject Areas : electrical and computer engineering
Mohsen Sarami
1
,
مجيد معظمي
2
*
,
غضنفر شاهقلیان
3
1 -
2 -
3 - دانشگاه آزاد اسلامي واحد نجفآباد
Keywords: Optimal operation, microgrid, renewable energies, energy storage, Salp swarm algorithm, power market, demand response,
Abstract :
Integrating renewable resources to provide local load has created a concept called microgrid. With the widespread introduction of microgrids, energy management and system utilization and resources in the electricity market are important tasks of microgrid management. In this paper, the problem of microgrid utilization is modeled taking into account economic, technical and uncertainties related to power consumption, wind speed and solar radiation in electricity market conditions. One of the most important issues in the electricity market is the discussion of the participation of units in real price conditions. In this paper, a framework for the exploitation of electricity and the consumption of controllable loads through integrated utilization of distributed energy sources of uncertainty is presented from a consumer perspective. The optimization problem is a two-step stochastic linear programming that minimized the cost of microgrid operation and expected cost of consumers considering the consumer’s requirement for controllable loads in the desire time interval and distribution company constraints that solved by using Salp swarm optimization algorithm. RBT and IBR tariffs are employed for modeling retail power market for better reflection of wholesale price volatility and avoid of the concurrent use of consumers. In this method price announced to the consumers by retailers only is limited specific later hours instead of the entire operation period. In this condition any timing of controllable loads need to price forecasting, while this forecasting have some uncertainties. These uncertainties are modeled using Monte Carlo method for stochastic price variable scenario generation. MATLAB software is employed for simulation and verification of the proposed method.
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