Meta-heuristic optimization methods applied to renewable distributed generation planning: A review

Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium–low voltage distribution networks

Incorporating large-scale renewable distributed generation (RDG) into the power grid introduces significant challenges associated with the indeterminacy of the power system, significantly impacting the comprehensive integration and efficient utilization of the distribution network. This underscores the imperative to investigate scientific evaluation methods and mechanisms for enhancing the RDG absorptive capacity within the distribution network. In light of the stochastic and temporal characteristics inherent to RDG, a novel approach for assessing the RDG hosting capacity within distribution networks is proposed, taking temporal scenarios into account. This method optimizes the RDG output probability distribution model based on its temporal fluctuation change rule, and constructs RDG temporal scenario model by using scenario reduction technology. Subsequently, a temporal scenarios-based RDG hosting capacity assessment model is established, taking into account active network management (ANM) techniques. The effectiveness of the proposed methodology is substantiated through simulation results conducted on a modified IEEE 33-bus system.

Due to intermittent renewable energy and fluctuating load demand, distribution networks with renewable distributed generation (DG) installations are more likely to suffer voltage issues and significant power losses. The performance of conservation voltage regulation (CVR) schemes may be adversely affected by the undesirable voltage profile at specific nodes. This paper aims to reduce power losses in CVR‐implemented networks by optimally planning new renewable DGs without changing the existing ones. A scenario‐based optimal renewable DG planning model is proposed with a novel scenario formation method. The uncertainties of load demand and renewables are captured jointly and formed into a finite number of scenarios based on a multivariate Gaussian mixture model (MultiGMM). The locations and capacities of different types of new renewable DGs are optimally planned for CVR performance improvements on power loss saving by aggregating the operation status and probabilities of the scenarios using mixed‐integer non‐linear programming (MINLP). A time‐series simulation is formulated for accuracy verification. The results of case studies show that the proposed model can significantly reduce power losses, active load demand, and reactive load demand. The accuracy of the planning results can be guaranteed with fewer scenarios compared to a widely used classical scenario‐based planning method.

In this article we present a comparison of the performance between a metaheuristic optimization method, Game of Patterns (GofP), so-called by the author, and the well-known genetic algorithms (GAs), through two implementations, namely: the GA of Scilab (SGA); and the GA of the R Project for Statistical Computing (RGA). For this purpose, we have selected a set of multimodal objective functions in the n-dimensional Euclidean space \(\mathbb {R}^{n}\) with a unique global minimum. For comparing both metaheuristic optimization approaches, a performance indicator of quality, denoted Q(p, n, s), was defined, which allows us to measure the quality of the obtained global optimal solution for each pth problem, in the n-dimensional space, when it is solved by each metaheuristic optimization method \(s\in \{\texttt {GofP},\texttt {SGA},\texttt {RGA}\}\). The indicator Q(p, n, s) then depends on: the number of evaluations of the pth optimization problem in the Euclidean space \(\mathbb {R}^{n}\), which has required the s metaheuristic optimization method for identifying the global minimum; and the distance between the location of its respective unique global minimum and the location of the minimum that has been identified by the s metaheuristic optimization method. The paper also offers a brief explanation of the GofP method, which has been developed for solving unconstrained mixed integer problems in the \(n\times m\)-dimensional Euclidean space \(\mathbb {R}^{n}\times \mathbb {Z}^{m}\).

Many countries are accepting the Kyoto Protocol to the United Nations Framework Convention on Climate Change (UNFCCC) in other to reduce the emission of carbon dioxide and other greenhouse gases. Renewable distributed generation (RDG) has become one of the most effective ways to reach this goal. It has been recognized as an alternative way of generating clean energy around the globe today. There has been an increase in the number of integrated RDG to the grid and also within the microgrids in other to support the power needed by the load during crisis. Current literature highlights voltage dips to be a serious problem, which has serious cost implication whenever they occur. One of the corrective methods has been mitigation through renewable distribution generation. This paper demonstrates how renewable distributed generation can be used to mitigate voltage dip as a result of faults from both the external grid and within the microgrid.

This study proposes a probabilistic dynamic model for multi-objective distributed generation (DG) planning, which also considers network reinforcement at presence of uncertainties associated with the load values, generated power of wind turbines and electricity market price. Monte Carlo simulation is used to deal with the mentioned uncertainties. The planning process is considered as a two-objective problem. The first objective is the minimisation of total cost including investment and operating cost of DG units, the cost paid to purchase energy from main grid and the network reinforcement costs. The second objective is defined as the minimisation of technical risk, including the probability of violating the safe operating technical limits. The Pareto optimal set is found using non-dominated sorting genetic algorithm method and the final solution is selected using a max–min method. The model is applied on two distribution networks and compared with other models to demonstrate its effectiveness.

Solving non-convex economic load dispatch problem via artificial cooperative search algorithm

Future smart electrical distribution systems could adopt switch remote control and advanced automation systems to operate in autonomous mode (islanding) some portions of the network in order to improve system reliability. In this perspective, a free code able to assess distribution reliability by using analytical or Monte Carlo simulation methods accounting for intentional islanding operation is presented. An effective network representation and data structure, which have led up to the implementation of two automatic and general procedures, are proposed. The procedures share common underlying information in order to speed up calculation. A further merit of the tool is that it enables the user to quickly and simply create the input files. The reliability indices of three distribution networks have been computed by means of the developed software for different scenarios, that is when island mode of operation is not permitted by regulation, and when island mode is permitted (intentional islanding) with the local load either partially or totally supplied by renewable distributed generators. The free code, the input files and the results related to the considered networks are available online at: http://www.dieei.unict.it/users/sconti/psrs.htm.

Renewable distributed generators (RDGs) are widely embedded in electrical distribution networks due to their economic, technological, and environmental benefits. However, the main problem with RDGs, photovoltaic generators, and wind turbines, in particular, is that their output powers are constantly changing due to variations in sun irradiation and wind speed, leading to power system uncertainty. Such uncertainties should be taken into account when selecting the optimal allocation of RDGs. The main innovation of this paper is a proposed efficient metaheuristic optimization technique for the sizing and placement of RDGs in radial distribution systems considering the uncertainties of the loading and RDG output power. A Monte Carlo simulation method, along with the backward reduction algorithm, is utilized to create a set of scenarios to model these uncertainties. To find the positions and ratings of the RDGs, the artificial gorilla troops optimizer (GTO), a new efficient strategy that minimizes the total cost, is used to optimize a multiobjective function, total emissions, and total voltage deviations, as well as the total voltage stability boosting. The proposed technique is tested on an IEEE 69-bus network and a real Egyptian distribution grid (East Delta Network (EDN) 30-bus network). The results indicate that the proposed GTO can optimally assign the positions and ratings of RDGs. Moreover, the integration of RDGs into an IEEE 69-bus system can reduce the expected costs, emissions, and voltage deviations by 28.3%, 52.34%, and 66.95%, respectively, and improve voltage stability by 5.6%; in the EDN 30-bus system, these values are enhanced by 25.97%, 51.1%, 67.25%, and 7.7%, respectively.

Environmental concerns and depletion of traditional energy lead to the booming development of renewable distributed generation (RDG) in the past decade. However, due to intermittent nature of renewable energy sources, to what extent RDG could provide capacity to power systems becomes a critical issue to the utility company when implementing long-term system strategic (generation expansion) planning. On the other hand, in a smart-grid frame, the popularization of different varieties of demand-side resources enables the system to operate at more flexible modes ever before. The potential variability in load demand not only introduces additional dynamics and uncertainties to the system, but could also affect the reliability benefits of RDG. Therefore, in practice, to effectively estimate the reliability value of RDG in power systems, the potential interaction between generation- and demand-side must be properly captured. In this paper, a study to assess the capacity credit (CC) of RDG in a context of distributed generation system is performed with consideration of the impacts of demand response (DR). A compound reliability model for DR is presented, which considers the uncertainties involved in both instant response and follow-on load recovery processes. On this basis, an assessment framework for the CC of RDG based on sequential Monte Carlo simulation is developed by which the inter-temporal characteristics of DR resources can be fully captured. The numerical study is implemented based on the IEEE-38 bus test case. The calculation results demonstrate that the CC of RDG would depend on a variety of factors, including penetration level, responsiveness of load demand and the correlations between RDG and DR availability. Also, it is shown that accounting for the underlying effect of DR is of absolute importance, otherwise the CC of RDG might be estimated erroneously in real practices.

The penetration of plug-in electric vehicles and renewable distributed generation is expected to increase over the next few decades. Large scale unregulated deployment of either technology can have detrimental impact on the electric grid. However, appropriate pairing of these technologies along with some storage could mitigate their individual negative impacts. This paper presents a framework and an optimization methodology for designing grid-interfaced systems that integrate electric vehicle chargers, distributed generation and storage. To demonstrate its usefulness, this methodology is applied to the design of optimal architectures for a residential charging case. It is shown that, given current costs, maximizing grid power usage minimizes system lifecycle cost. However, depending upon the location's solar irradiance patterns, architectures with solar photovoltaic generation can be more cost effective than architectures without.

Optimal stochastic scenario-based allocation of smart grids’ renewable and non-renewable distributed generation units and protective devices

The communication requirement for integrating Distributed Renewable Generation (DRG) into Smart Grid (SG) is not strict, where the reliability and critical demand of data delivery are compromised due to the low-data rate and power of ZigBee. However, the presence of various dielectric constant materials in the DRG can cause transmission impairments of the electromagnetic wave. In this paper, we have analysed the performance of the ZigBeePRO network by applying the shortest path algorithm while delivering energy data from the solar DRG to the SG. The DRG architecture is created by considering a real test-bed of 35 kW solar DRG at Universiti Putra Malaysia (UPM). The numbers of nodes are calculated from specifications of the ZigBeePRO enabled Waspmote embedded board, inverters, and electrical parameters of a Photovoltaic (PV) module. The results of the obtained propagation path loss model indicate that the Transverse Electric (TE) and Transverse Magnetic (TM) polarizations are proportional to the loss of the propagation path at different incident angles (α); however, an exception is observed for the TM polarization at α = 55 o. Due to this polarization effect, the brick-built type cabin at the DRG site is a consequence of a higher propagation path loss than the Iron (III)-made cabin. The other performance parameters, including network throughput, data loss, and ZigBeePRO collision, are also evaluated.

Multi-stage active management of renewable-rich power distribution network to promote the renewable energy consumption and mitigate the system uncertainty

Distributed generator-based distribution system service restoration strategy and model-free control methods