Environmental concerns, global warming and fossil fuel prices are creating a shift in the expectations of consumers and industries to move toward renewable energy resources. However, the inability to control the output of renewable resources, like wind and solar, results in operational challenges in power systems. The operational challenges of renewable resources can be met by energy storage systems. The energy storage systems scheduling can be used to control the effect of intermittent renewable energy resources. Furthermore, energy storage systems can be used for ancillary services, peak reduction, and mitigating contingencies in the distribution and transmission networks [1].Distributed photovoltaic (DPV) rooftop panels are considered as renewable energy resources in this paper. Depending on the DPV size and solar irradiation, DPV adoption can create problems for the distribution network. In addition, utility companies have to pay different prices for electricity during different times of the day due to the dynamic electricity market. Therefore, the DPV adoption can be controlled with the help of real-time electricity price and the load profile.Facing these challenges, this paper presents an operational optimization algorithm for a Distributed Energy Storage (DES) system. The DES system presents a fleet of batteries connected to distribution transformers. The DES can be used for withholding DPV power before it is bid into the market. Withholding DPV generation represents a gaming method to realize higher revenues due to the time varying cost of electricity. Energy storage systems may be used to control DPV power variation and thus help distribution network operations[2].The objective of this paper is to present a DES optimal economic control system to improve the DPV adoption in power distribution networks. The control system decisions depend on the load profiles, and the real-time Locational Marginal Price (LMP).Economic operation of the DES is a complex problem because of the time dependency of the battery capacity (where sufficient energy reserves must be maintained in case of power loss), the solar irradiation uncertainty, and the real-time electricity price variability. The mathematical approach used is the Discrete Ascent Optimal Programming (DAOP) algorithm. An advantage of DAOP is its assurance of convergence after a finite number of computational iterations.
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