Bi-Level Programming Approach for the Optimal Allocation of Energy Storage Systems in Distribution Networks

Shi, Nongnong; Luo, Yimo

doi:10.3390/app7040398

Cited by 17 publications

(12 citation statements)

References 37 publications

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“…Optimal operation of the entire power system should enable the decision makers to optimize their respective objective functions independently while simultaneously cooperating with one another. Thus, several researchers have studied bi-level optimization models to address this issue [21][22][23]. However, only the DN operation has been optimized in these studies, without the tie-line control being considered in the analysis.…”

Section: Literature Review and Motivationmentioning

confidence: 99%

Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band

Kim

2019

Applied Sciences

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An optimal operation of new distributed energy resources can significantly advance the performance of power systems, including distribution network (DN). However, increased penetration of renewable energy may negatively affect the system performance under certain conditions. From a system operator perspective, the tie-line control strategy may aid in overcoming various problems regarding increased renewable penetration. We propose a bi-level optimization model incorporating an energy band operation scheme to ensure cooperation between DN and microgrid (MG). The bi-level formulation for the cooperation problem consists of the cost minimization of the DN and profit maximization of the MG. The goal of the upper-level is to minimize the operating costs of the DN by accounting for feedback information, including the operating costs of the MG and energy band. The lower-level aims to maximize the MG profit, simultaneously satisfying the reliability and economic targets imposed in the scheduling requirements by the DN system operator. The bi-level optimization model is solved using an advanced method based on the modified non-dominated sorting genetic algorithm II. Based on simulation results using a typical MG and an actual power system, we demonstrate the applicability, effectiveness, and validity of the proposed bi-level optimization model.

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Section: Literature Review and Motivationmentioning

confidence: 99%

Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band

Kim

2019

Applied Sciences

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“…f 1 = min Cost1 + C loss + 0 i f voltage is within limits C pun i f the voltage is outside the limits (8) where C loss is the cost of line losses which can be calculated by the active power of the line loss multiplied by the average electricity price and C pun is the cost of punishment applied by the utility for unsolved voltage issues. The objective of the optimization is to minimize the ESS capital cost and line loss cost at this stage, so the fitness function of the GA can be represented by the reciprocal of the objective function as Equation (9).…”

Section: The First-stage Optimizationmentioning

confidence: 99%

“…The optimal sitting and sizing of the ESSs were determined by considering the Distributed Generation (DG) utilization, network losses, and voltage profiles [6,7]. In a study by Shi and Luo, a bi-level energy storage planning model for energy storage capacity, and a location configuration algorithm was developed by taking reliability into account, in particular, accommodating the intermittence brought by renewables [8]. At present, the high-cost of ESSs impede the wide-area deployment, consequently, the cost minimization of ESS become a main issue in energy storage planning.…”

Section: Introductionmentioning

confidence: 99%

Considering the Life-Cycle Cost of Distributed Energy-Storage Planning in Distribution Grids

Zhu

et al. 2018

Applied Sciences

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In the face of the radical revolution of energy systems, there is a gradually held consensus regarding the adoption of distributed renewable energy resources, represented by Photovoltaic (PV) and wind generation. Consequently, the distributed Energy Storage Systems (ESSs) have become increasingly important in the distribution networks, as they provide the arbitrage and ancillary services. Determining the optimal installation site and the capacity of the distributed ESSs will defer the network reinforcements, reduce the investment of ESSs, and improve the reliability, flexibility, and efficiency of distribution grids. In order to investigate the optimal ESS configuration and to solve voltage fluctuations brought by the increased penetration of PV, in this study a two-stage heuristic planning strategy has been proposed, which considers both the economic operation and the lifetime of the distributed ESSs, to determine the optimal sitting and sizing of the ESSs, in the distribution grids. The first stage decides the optimal installation site and the economic scheduling of the ESSs, aiming to minimize the fabricating cost of the distributed ESSs and the network losses. Based on the output of the first stage, the second stage planning is further delivered to achieve the optimal ESS capacity, considering the Life-Cycle Cost (LCC) minimization. Finally, the feasibility and effectiveness of the proposed method is verified on a typical distribution case study network.

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“…The charging and discharging status of energy storage is limited not only by the capacity of grid-connected devices but also by the state of charge (SOC) of energy storage [41]. Assuming that the charging and discharging efficiency remain unchanged during operation, the energy storage operation is constrained as:…”

Section: Energy Storage Operation Constraintsmentioning

confidence: 99%

Multiobjective Scheduling of an Active Distribution Network Based on Coordinated Optimization of Source Network Load

et al. 2018

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With the development of active distribution networks, the means of controlling such networks are becoming more abundant, and simultaneously, due to the intermittency of renewable energy and the randomness of the demand-side load, the operating uncertainty is becoming serious. To solve the problem of source–network–load coordination scheduling, a multiobjective scheduling model for an active distribution network (ADN) is proposed in this paper. The operating cost, renewable energy utilization rate, and user satisfaction are considered as the optimization objectives, and the distributed generation (DG) output power, switch number, and incentive price for the responsive load are set as the decision variables. Then the probabilistic power flow based on Monte Carlo sampling and the chance-constrained programming are used to deal with the uncertainty of the ADN. Moreover, the reference point–based many-objective evolutionary algorithm (NSGA3) is used to solve this nonlinear, multiperiod, and multiobjective optimization problem. The effectiveness of the proposed method is verified in the modified IEEE 33-bus distribution system. The results show that the proposed scheduling method can effectively improve the system status.

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Bi-Level Programming Approach for the Optimal Allocation of Energy Storage Systems in Distribution Networks

Cited by 17 publications

References 37 publications

Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band

Optimal Operational Scheduling of Distribution Network with Microgrid via Bi-Level Optimization Model with Energy Band

Considering the Life-Cycle Cost of Distributed Energy-Storage Planning in Distribution Grids

Multiobjective Scheduling of an Active Distribution Network Based on Coordinated Optimization of Source Network Load

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