Optimal siting and sizing of distributed energy storage systems via alternating direction method of multipliers

Energy Storage Systems (ESSs) has an important role in Active Distribution Networks (ADNs). Within this context this paper focuses on the problem of ESSs optimal siting and sizing. Following similar approaches already proposed by the Authors, this paper uses a multi-objective procedure to account various ancillary services that can be provided by ESSs. The proposed procedure takes into account the voltage support and network losses minimization along with minimization of the cost of energy from external grid. For the case of large-scale problems, accounting networks with large number of nodes and scenarios, the selection of the solution methodology is a non-trivial problem. In this respect, the paper proposes and discusses the applicability of the Alternative Direction Method of Multipliers in order to provide an efficient algorithm for large-scale networks that also provide a solution to the optimality aspect. A real large-scale network with real profiles of load and distributed photovoltaic generation is used as the case study to analyze the effectiveness of the proposed methodology

Special issue on developing and implementing smart grids : novel technologies, techniques and models

fi=vertaisarvioimaton|en=nonPeerReviewed

Exact Convex Modeling of the Optimal Power Flow for the Operation and Planning of Active Distribution Networks with Energy Storage Systems

The distribution networks are experiencing important changes driven by the massive integration of renewable energy conversion systems. However, the lack of direct controllability of the Distributed Generations (DGs) supplying Active Distribution Networks (ADNs) represents a major obstacle to the increase of the penetration of renewable energy resources characterized by a non-negligible volatility. The successful development of ADNs depends on the combination of i) specific control tools and ii) availability of new technologies and controllable resources. Within this context, this thesis focuses on developing practical and scalable methodologies for the ADN planning and operation with particular reference to the integration of Energy Storage Systems (ESSs) owned, and directly controlled, by the Distribution Network Operators (DNOs). In this respect, an exact convex formulation of Optimal Power Flow (OPF), called AR-OPF, is first proposed for the case of radial power networks. The proposed formulation takes into account the correct model of the lines and the security constraints related to the nodal voltage magnitudes, as well as, the lines ampacity limits. Sufficient conditions are provided to guarantee that the solution of the AR-OPF is feasible and optimal (i.e., the relaxation used is exact). Moreover, by analyzing the exactness conditions, it is revealed that they are mild and hold for real distribution networks. The AR-OPF is further augmented by suitably incorporating radiality constraints in order to develop an optimization model for optimal reconfiguration of ADNs. Then, a two-stage optimization problem for day-ahead resource scheduling in ADNs, accounting for the uncertainties of nodal injections, is proposed. The Adaptive Robust Optimization (ARO) and stochastic optimization techniques are successfully adapted to solve this optimization problem. The solutions of ARO and stochastic optimization reveal that the ARO provides a feasible solution for any realization of the uncertain parameters even if its solution is optimal only for the worst case realization. On the other hand, the stochastic optimization provides a solution taking into account the probability of the considered scenarios. Finally, the problem of optimal resource planning in ADNs is investigated with particular reference to the ESSs. In this respect, the AR-OPF and the proposed ADN reconfiguration model, are employed to develop optimization models for the optimal siting and sizing of ESSs in ADNs. The objective function aims at finding the optimal trade-off between technical and economical goals. In particular, the proposed procedures accounts for (i) network voltage deviations, (ii) feeders/lines congestions, (iii) network losses, (iv) cost of supplying loads (from external grid or local producers) together with the cost of ESS investment/maintenance, (v) load curtailment and (vi) stochasticity of loads and renewables production. The use of decomposition methods for solving the targeted optimization problems with discrete variables and probable large size is investigated. More specifically, Benders decomposition and Alternative Direction Method of Multipliers (ADMM) techniques are successfully applied to the targeted problems. Using real and standard networks, it is shown that the ESSs could possibly prevent load and generation curtailment, reduce the voltage deviations and lines congestions, and do the peak shaving

Planning Models for Distribution Grid

planning and expansion models of the electrical grid to guarantee mainly the energy supply for current and future loads and the technical and operational criteria of the grid, requiring for this, to make an orderly sequence of investments along the planning horizon. Nevertheless, the ongoing integration of distributed generation and energy storage devices has been leading to consider some changes on those models focus to deal with the complexity those integrations bring with them. In this context, this paper presents a brief review of distribution planning and expansion models with the perspective of identifying approaches, objective functions used, uncertainties analyzed, the computational and evolutionary techniques used, the dimension of the grid under analyses, among other aspects considered to solve the planning and expansion problem. This document will provide a background to find out the further works in this field

Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm

The deployment of utility-scale energy storage systems (ESSs) can be a significant avenue for improving the performance of distribution networks. An optimally placed ESS can reduce power losses and line loading, mitigate peak network demand, improve voltage profile, and in some cases contribute to the network fault level diagnosis. This paper proposes a strategy for optimal placement of distributed ESSs in distribution networks to minimize voltage deviation, line loading, and power losses. The optimal placement of distributed ESSs is investigated in a medium voltage IEEE-33 bus distribution system, which is influenced by a high penetration of renewable (solar and wind) distributed generation, for two scenarios: (1) with a uniform ESS size and (2) with non-uniform ESS sizes. System models for the proposed implementations are developed, analyzed, and tested using DIgSILENT PowerFactory. The artificial bee colony optimization approach is employed to optimize the objective function parameters through a Python script automating simulation events in PowerFactory. The optimization results, obtained from the artificial bee colony approach, are also compared with the use of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS placement approach can successfully achieve the objectives of voltage profile improvement, line loading minimization, and power loss reduction, and thereby significantly improve distribution network performance

Grey wolf optimization-based optimum energy-management and battery-sizing method for grid-connected microgrids

In the revolution of green energy development, microgrids with renewable energy sources such as solar, wind and fuel cells are becoming a popular and effective way of controlling and managing these sources. On the other hand, owing to the intermittency and wide range of dynamic responses of renewable energy sources, battery energy-storage systems have become an integral feature of microgrids. Intelligent energy management and battery sizing are essential requirements in the microgrids to ensure the optimal use of the renewable sources and reduce conventional fuel utilization in such complex systems. This paper presents a novel approach to meet these requirements by using the grey wolf optimization (GWO) technique. The proposed algorithm is implemented for different scenarios, and the numerical simulation results are compared with other optimization methods including the genetic algorithm (GA), particle swarm optimization (PSO), the Bat algorithm (BA), and the improved bat algorithm (IBA). The proposed method (GWO) shows outstanding results and superior performance compared with other algorithms in terms of solution quality and computational efficiency. The numerical results show that the GWO with a smart utilization of battery energy storage (BES) helped to minimize the operational costs of microgrid by 33.185% in comparison with GA, PSO, BA and IBA

Optimal planning of photovoltaic distributed generation considering uncertainties using monte carlo pdf embedded MVMO-SH

In recent years, photovoltaic distributed generation (PVDG) has seen rapid growth due to its benefits in supporting the power system network, enhancing the transmission and distribution of power, and minimizing power congestion. PVDGs are connected directly to the load and produce power locally for the users, thus help to relieve the entire grid by reducing the demand especially during the peak load. Due to the random nature of the weather and occurrences of uncertainty, the planning and optimization of PVDG in the power system network with predicted uncertainty in photovoltaic generations and load variations are of crucial importance to minimize power losses. Thus, this research aims to develop a new optimization framework based on Monte Carlo embedded hybrid variant mean – variance mapping optimization (MVMO-SH) for the planning of PVDGs by considering these uncertainties. In this work, the probabilistic method in managing the risk of solar irradiance uncertainty with load variability is prepared. Uncertainty management is focused on the Malaysian tropical climate. Using meteorological data for one reference year, the Monte-Carlo simulation is performed in the Beta probability density function (PDF) to model continuous random variables of solar irradiances. For the load modelling studies, the Monte Carlo simulation is performed in Gaussian PDF to develop a probability model of various types of loads. The urban residential, commercial and industrial load profiles for one reference year are used for the load modelling. The probabilistic values of PV generation and load models are employed as the input data to the load flow analysis for the radial distribution network. The load flow patterns will significantly have affected when uncertain PV generation – load models are considered into the power flow algorithm. A new method of probabilistic backward – forward sweep power flow (BFSPF) based on Monte Carlo – PDF is developed as the fitness evaluation for the PVDG planning. A hybrid population – based stochastic optimization method named MVMO-SH algorithm is proposed to optimize PVDG locations and sizes in the grid system network. The objective function is to minimize the active power loss (APL) index. The proposed algorithm is applied to the standard radial test system to examine the usefulness and effectiveness of the proposed method. The impacts of PVDG on the power system network have been examined. As the results of the study, the uncertainty model of solar irradiance in Monte Carlo – Beta PDF has shown an almost similar pattern with less than 15% deviation as compared to the model from SEDA. The reductions in the power system’s total power losses have been shown with appropriate planning of PVDG in the power system network considering uncertainty in PV generation and load variations based on the Malaysian Tropical climate. When probabilistic BFSPF is optimized by MVMO-SH embedded Monte Carlo – PDF under uncertainties, the results show a better APL index compared to utilizing PSO and GA. The results also revealed that the uncertainties had the greatest influence on the optimal planning of PVDG in the power system network