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Abstract
This paper presents two economic criteria for guiding the energy storage system (ESS) sizing in grid-connected microgrids. The internal power output model and the economic operation model of ESS are firstly established. Then, the combination of heuristic adjustment strategy and hybrid particle swarm optimization algorithm are introduced to solve the optimal operation model of ESS. Then according to the ESS life model and cost-benefit analysis, a static investment economic criterion which is easy and simple to be calculated is proposed to demonstrate the economic feasibility of ESS investment programs in the short term. Considering the time value of currency, a dynamic investment economic criterion is proposed later for long-term investment projects. Furthermore, the ESS sizing boundary of achieving profits could be also obtained according to the criteria which can indicate the economic attractiveness or resistance to ESS investors in the microgrid. A case study has verified its effectiveness. At the same time, sensitivity analysis is given to show the impact on key parameters, such as investment unit price and electricity purchase price on ESS investment.
Highlights
Variable renewable energy (VRE), e.g., wind turbine (WT) power generation and photovoltaic (PV) power generation, is characterized by intermittence, randomness, and uncertainties
A local microgrid was utilized as the test system, in which PV and WT units were installed inside
Two economic criteria were proposed to are judge the economic feasibility of energy storage system (ESS) into microgrid
Summary
Variable renewable energy (VRE), e.g., wind turbine (WT) power generation and photovoltaic (PV) power generation, is characterized by intermittence, randomness, and uncertainties. The characteristics of power systems have changed dramatically, and the integration of large-scale VRE has brought more and more challenges to the safe and reliable operation of the power grid. To solve this problem, energy storage technology has been extensively studied as an effective means to mitigate the fluctuation of VRE generation. After the introduction of energy storage in the system, the demand side management can be effectively realized, the peak-to-valley difference at night can be eliminated, and the load can be smoothed
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