Inverter-Dominated Networked Microgrids with Marine Energy Resources and Energy Storage Systems for Coastal Community Resiliency Enhancement

2021 ◽  
Author(s):  
Yuxi Men ◽  
Yuhua Du ◽  
Xiaonan Lu ◽  
Jianzhe Liu ◽  
Feng Qiu
Keyword(s):  
Energy Storage ◽  
Storage Systems ◽  
Energy Resources ◽  
Energy Storage Systems ◽  
Coastal Community ◽  
Marine Energy ◽  
Community Resiliency ◽  
Networked Microgrids ◽  
Resiliency Enhancement

Robust Particle Swarm Optimization for Active/Reactive and Reserve Scheduling in a Grid-connected Microgrid With Energy Storage Systems

2021 ◽  
Author(s):  
Ali Rasouli ◽  
Mehdi Bigdeli ◽  
Abouzar Samimi
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Social Welfare ◽  
Objective Function ◽  
Optimization Model ◽  
Storage Systems ◽  
Ancillary Services ◽  
Energy Resources ◽  
Energy Storage Systems ◽  
Swarm Optimization

Abstract Background: In recent years, simultaneous participation in electrical energy and ancillary services markets has been very profitable for distributed energy resources (DERs). Moreover, the presence of renewable generations along with energy storage systems (ESS) is bringing a significant contribution to modern distribution systems. High penetration of non-predictable power sources in microgrids (MGs), due to the uncertainties of these products, increases the need for ancillary services and the management and coordination of these technologies combined with the ESSs. Results: For the first time, this paper develops a robust particle swarm optimization model to handle the uncertain renewable power production involved in the joint active/reactive and reserve scheduling of a smart MG. The robust optimization approach has a medium priority compared to deterministic and stochastic ones. The objective function utilized for the optimal joint active/reactive and reserve scheduling of an MG is defined as maximizing social welfare, which is accomplished based on a max-min optimization model. The robust optimal solution can be achieved in such a way that the maximizer at the outer level makes an optimal decision against the worst-case objective function, which is acquired based on the minimizer at the inner level considering the uncertainty neighborhood. Conclusions: The effectiveness of the proposed method is examined on a 33-bus MG test system. Simulation results prove that the proposed RPSO model can help MG operators to reduce scheduling costs to obtain a higher social welfare. The consideration of more uncertainty in renewable energy resources production leads to higher operation costs, especially reserve costs. Integration of robustness against uncertainty in the joint active/reactive and reserve management in the smart MGs leads to a more robust operation at the expense of higher costs.

Distributed Energy Storage Control for Optimal Adoption of Solar Energy in Residential Networks

2012 ◽  
Author(s):  
Reza Arghandeh ◽  
Robert Broadwater
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Real Time ◽  
Storage Systems ◽  
Energy Resources ◽  
Solar Irradiation ◽  
Renewable Energy Resources ◽  
Distributed Energy ◽  
Energy Storage Systems ◽  
Distributed Energy Storage

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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