Sizing Of Renewable Energy Sources Research Articles

Sizing distributed energy resources in a renewable energy community with a grid-aware internal market structure

This paper proposes a cooperative approach aimed at distributed energy resources sizing in a renewable energy community, with considerations of the community’s optimal operation, impact on the electrical grid and an allocation of the benefits to its members. To this purpose, multiple investment modes are evaluated via a two-step procedure. In the first step, the size of renewable energy sources is determined by solving an optimization problem that maximizes community welfare, considering network and investments. In the second step, an optimization problem maximizing additional community member profit with price regularization is solved. This step shares benefits among community members. The potential of the proposed procedure is illustrated using a benchmark Dickert-LV network. This is a full y cooperative framework where the community operator is ensuring adequate grid operation, operational planning and sizing of new investments.

Optimal multi-objective sizing of renewable energy sources and battery energy storage systems for formation of a multi-microgrid system considering diverse load patterns

Optimal microgrid design is pivotal in planning active distribution networks (ADNs) with intermittent renewable energy sources (RESs) and battery energy storage systems (BESSs). This paper introduces an innovative approach to clustering existing ADN systems, incorporating RESs and BESSs into a set of microgrids (MGs) termed a multi-microgrid (MMG). The approach accommodates diverse load patterns, considering the dynamic nature of residential, commercial, and industrial loads. The proposed methodology employs an innovative k-means algorithm that utilizes end node characteristics based on the graph partitioning method and the Silhouette technique, transforming the existing system into an MMG framework. Furthermore, an iterative pareto-fuzzy (IPF) method is suggested to determine optimal RES and BESS sizes, considering reliability, total cost, and the unutilized surplus power function. The framework integrates total line losses into the generation adequacy assessment of energy management strategy (EMS) for each MG within the MMG system. The design and performance analysis of the proposed multi-objective optimization algorithm is tested on the IEEE 33-bus distribution system. The implementation of the independent operation index (IOI) and round-trip efficiency (RTE) metrics demonstrates the effectiveness of the proposed approach in constructing an MMG, with achieved values exceeding 90 percent for IOI and 77 percent for RTE.

Towards low-carbon power networks: Optimal location and sizing of renewable energy sources and hydrogen storage

This paper proposes a systematic optimization framework to jointly determine the optimal location and sizing decisions of renewables and hydrogen storage in a power network to achieve the transition to low-carbon networks efficiently. We obtain these strategic decisions based on the multi-period alternating current optimal power flow (AC MOPF) problem that jointly analyzes power network, renewable, and hydrogen storage interactions at the operational level by considering the uncertainty of renewable output, seasonality of electricity demand, and electricity prices. We develop a tailored solution approach based on second-order cone programming within a Benders decomposition framework to provide globally optimal solutions. In a test case, we show that the joint integration of renewable sources and hydrogen storage and consideration of the AC MOPF model significantly reduces the operational cost of the power network. In turn, our findings can provide quantitative insights to decision-makers on how to integrate renewable sources and hydrogen storage under different settings of the hydrogen selling price, renewable curtailment cost, emission tax price, and conversion efficiency.

A stochastic approach to determine the energy consumption and synthetic load profiles of different customer types of rural communities

The electricity demand is highly stochastic and unpredictable. This is due to the fact that it is significantly impacted by a number of factors, including the type of load, weather, time of day, seasonality, economic limitations, customers’ way of living, and other randomness factors. An accurate load model is one of the main inputs for the design of an economical and reliable renewable-based rural electrification system for rural communities and demand management systems. This paper presents a generic methodology for determining a rural community's energy consumption load profile, which is used to determine the most cost-effective size of renewable energy sources for rural electrification purposes. To determine the load profile parameters, such as the types of appliances used, their functioning times, functioning windows, and expected minimum and maximum cycle time, a field survey was conducted in four rural electrified Ethiopian villages. Since the survey findings will not fully explain the stochastic nature of the load profile, the load parameters are randomly generated, and a bottom-up approach is used to estimate the rural community's energy usage. In this study, household loads, public institution loads, business loads, and small industrial loads are the main consumer types taken into account. These loads are classified according to their energy usage as weekdays, weekends, and national and religious holidays. A MATLAB program is developed and implemented to obtain the load profiles of different customer groups. The results of this study are assessed in accordance with the multi-tier criterion and verified through the use of the well-known software HOMER Pro- and LoadProGen.

Socio‐techno‐economic‐environmental sizing of hybrid renewable energy system using metaheuristic optimization approaches

AbstractElectricity supply reliability in an electricity distribution network is majorly affected due to unexpected power failures and power cuts. A hybrid renewable energy system (HRES) with the optimal size of renewable energy sources can substantially improve power reliability. Therefore, this article develops an objective function incorporating socio‐techno‐economic‐environmental (STEE) factors for HRES optimal sizing to supply reliable power to a rural village. The factors considered in the objective function are namely social (employment generation factor, human progress index, and land cost), technical (excess energy factor, renewable energy portion, and loss of power supply probability), economical (total net present cost, cost of energy, and annualized cost of system), and environmental (emission cost). In this article, for the first time, marine predators algorithm (MPA) based metaheuristic optimizer is devised to address the sizing optimization problem of HRES. Three HRES configurations, having different arrangements of diesel generator (DG), biogas generator (BG), battery (BAT), wind turbine (WT), and photovoltaic (PV), are examined utilizing MPA, particle swarm optimization (PSO), salp swarm algorithm (SSA), and genetic algorithm (GA) for optimal configuration. Due to the lowest value of economical and environmental factors and the highest value of the social factor, the PV‐WT‐BAT‐BG‐DG configuration is optimal compared to other investigated configurations with MPA. Comparing the four optimizers, MPA has the best STEE factor values, as well as stronger convergence, greater ability to escape from local minima, and higher ability to approach the global optimum. Additionally, by contrasting it with the PSO result for the three HRES configurations, the MPA result quality is confirmed. Furthermore, the cost of energy (0.1799 $/kWh) of the optimal configuration is less than the latest addressed in the literature.

A Methodology to Evaluate Reactive Power Reserves Scarcity During the Energy Transition

The lack of reserves for reactive power production and absorption is an envisioned, still basically unexplored, threat to the voltage profile adequacy and thereby secure operation of transmission grids during the energy transition toward renewable-dominated power production. This paper proposes a novel, generic, comprehensive, and realistic methodology to identify when this issue of reactive power reserves (RPRs) scarcity during plausible scenarios of the energy transition would become severe. The computational core of the proposed methodology comprises four different AC security-constrained optimal power flow (SCOPF) problems: one conventional, two tailored ones that assess the RPRs scarcity in production and absorption modes, respectively, and an optimal reactive power dispatch. The methodology is versatile, offering the possibility to assess RPRs in different timescales, ranging from day-ahead short-term operation to years-ahead long-term operation, and considering appropriate renewable energy production forecasts and day-dependent load profiles. The proposed methodology can serve as a decision support tool for the transmission system operator (TSO), allowing to plug and play different plausible energy transition scenarios (e.g., differing in terms of sequence and timing of: phased out power plants as well as location, type, and size of renewable energy sources deployed) and ultimately informing the TSO about the timing where RPRs become insufficient to maintain security. Without loss of generality, the value of the proposed methodology is extensively demonstrated in a 60-bus Nordic32 system, considering 52 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N-1$</tex-math></inline-formula> line and generator contingencies, while the tractability of AC SCOPF problems is assessed in a 1,203-bus system.

Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems

The need for energy has significantly increased in the world in recent years. Various research works were presented to develop Renewable Energy Sources (RESs) as green energy Distributed Generations (DGs) to satisfy this demand. In addition, alleviating environmental problems caused by utilizing conventional power plants is diminished by these renewable sources. The optimal location and size of the DG-RESs significantly affect the performance of Radial Distribution Systems (RDSs) through the fine bus voltage profile, senior power quality, low power losses, and high efficiency. This paper investigates the use of PV (photovoltaic) and (Wind Turbine) WT systems as a DG source in RDSs. This investigation is presented via the optimal location and size of the PV and WT systems, which are the most used DG sources. This optimization problem aims to maximize system efficiency by minimizing power losses and improving both voltage profile and power quality using White Shark Optimization (WSO). This algorithm emulates the attitude of great white sharks when foraging using their senses of hearing and smell. It confirms the balance between exploration and exploitation to discover optimization that is considered as the main advantage of this approach in attaining the global minimum. To assess the suggested approach, three common RDSs are utilized, namely, IEEE 33, 69, and 85 node systems. The results prove that the applied WSO approach can find the best location and size of the RESs to reduce power loss, ameliorate the voltage profile, and outlast other recent strategies. Adding more units provides a high percentage of reducing losses by at least 93.52% in case of WTs, rather than 52.267% in the case of PVs. Additionally, the annual saving increased to USD 74,371.97, USD 82,127.257, and USD 86,731.16 with PV penetration, while it reached USD 104,872.96, USD 116,136.57, and USD 155,184.893 with WT penetration for the 33, 69, and 85 nodes, respectively. In addition, a considerable enhancement in the voltage profiles with the growth of PV and WT units was confirmed. The ability of the suggested WSO for feasible implementation was validated and inspected by preserving the restrictions and working constraints.

Probabilistic Planning of Distribution Networks With Optimal DG Placement Under Uncertainties

This research paper presents an efficient methodology for distribution network planning under an uncertain environment. As an extension of our previous work presented at the ECCE Asia 2021 conference, here optimal placement and sizing of Renewable Energy Sources (RES)-based Distributed Generations (DGs) are determined considering the generation and load uncertainties. In addition, the optimal tap settings of off-load tap changing transformers present in a network are also determined. Probabilistic non-linear optimization is solved with a sensitivity-based technique to minimize the distribution network losses and improve its voltage stability. The proposed methodology is implemented on standard test systems like the IEEE 69 bus and the Indian 85 bus networks. Further, to determine its real-world functionality, the methodology is tested on a practical radial distribution network of 88 buses present in a remote Froan island of Norway. When compared with existing techniques, the proposed methodology provides much more efficient network planning solutions with lesser power losses. Developed on free and open-source software platforms, it also provides a reliable and cost-effective alternative to network operators to determine their network robustness.

Optimal sizing of RES and BESS in networked microgrids based on proportional peer‐to‐peer and peer‐to‐grid energy trading

AbstractNetworked microgrids emerged from the growing deployment of microgrids in the distribution network. The coordinated operation of networked microgrids offers technical and economical benefits to microgrid owners, customers, grid/utility, and other stakeholders. The optimal capacity sizing of renewable energy sources and battery energy storage systems allows microgrids to minimize costs and maximize reliability. A multi‐objective optimization problem is developed for optimal sizing in a networked microgrid consisting of four different microgrids. The annual energy costs and loss of power supply probability index are taken as objectives. Peer‐to‐peer and peer‐to‐grid energy trading approaches are employed. The peer‐to‐peer energy trading among microgrids employs the proposed “proportional trading method” via a networked microgrid manager or aggregator. The multi‐objective optimization problem formulated is solved using Multi‐Objective Particle Swarm Optimization. The individual objective optimization results for annual energy cost and loss of power supply probability are also analyzed. The proposed method decreases the interaction between the grid and the MGs, and the usage of renewable energy sources is enhanced. The capacity of battery energy storage systems is lowered by 96%, 53.2%, 48.86%, 21% for respective microgrids in networked microgrids. The results of proportional peer‐to‐peer energy trading‐based multi‐objective optimization show that trading energy among microgrids minimizes annual energy cost by 0.75% while maintaining system reliability.

An intelligent energy management system for optimum design and real-time operation

&lt;span lang="EN-US"&gt;Planning and management of distribution networks has become a very difficult task, especially with the strong expansion of renewable energy sources (RES) which are intermittent in nature. Maintaining fluidity and reliability of real-time decisions while taking into consideration uncertainties related to production and increasing the profit of distribution network operators is the objective of the system proposed in this work. It is an intelligent energy management system dedicated to the management of grid-integrated RES and battery energy storage systems (BESS), composed of: i) a real-time control and data acquisition model, ii) a model for forecasting the intermittent parameters of RES based on neural networks, iii) a long-term planning model based on the optimal placement and size of RES and BESS, and iv) an hourly planning model for scheduling the energy distribution between energy sources. The non-dominated sorting genetic algorithm and the entropy-TOPSIS method (technique for order of preference by similarity to ideal solution) form the basic block of this model. To evaluate it, a modified IEEE 33 bus network was used for testing and the results, for short-term scheduling, proved that the system succeeds in maximizing profits and significantly minimizing CO&lt;sub&gt;2&lt;/sub&gt; emissions, in addition to power losses and voltage drops.&lt;/span&gt;

Multi-objective Function Optimization for Locating and Sizing of Renewable Energy Sources and Energy Storages in Radial Distribution Networks with Digital Directional Overcurrent Relays and Digital Dual-setting Directional Overcurrent Relays

Multi-objective Function Optimization for Locating and Sizing of Renewable Energy Sources and Energy Storages in Radial Distribution Networks with Digital Directional Overcurrent Relays and Digital Dual-setting Directional Overcurrent Relays

Optimal sizing for a grid-connected hybrid renewable energy system: A case study of the residential sector in Durban, South Africa

Many countries, including South Africa, have introduced policies and incentives to increase their renewable energy capacities in order to address environmental concerns and reduce pollutant emissions into the atmosphere. In addition, consumers in South Africa have faced the ever-increasing price of electricity and unreliability of the grid since 2007 due to the lack of sufficient electricity production. As a result, employing hybrid renewable energy systems (HRESs) have gained popularity. This research focuses on grid-connected HRESs based on solar photovoltaic (PV) panels and wind turbines as a potential way of reducing the dependency of residential sector consumers on the grid. It aims to identify the optimal sizing of renewable energy sources to be cost-effective for consumers over a certain period of time, using Durban as a case study. Two artificial intelligence methods have been used to obtain the optimal sizing for the available PV panels, wind turbines and inverters. The results shown that the combination of PV panels and battery storage can be a profitable option. A system using higher rated power PV panels can start to become profitable in a shorter lifetime, but employing batteries can only be cost-effective if a long enough lifetime is considered.

Frequency stability of the Israeli power grid with high penetration of renewable sources and energy storage systems

As countries worldwide are integrating more energy storage systems and renewable energy sources, it is important to examine how these impact the frequency stability of the grid. In this study we explore this question by focusing on Israel in 2025. Based on the Israeli power grid model in 2025, which includes detailed information on the entire transmission network, generation units, and loads, we examine hundreds of different locations and sizes of renewable energy sources and energy storage systems, focusing on the frequency behavior in each scenario following the loss of a large generator. This is done using the industry-standard PSS/E simulator. The results lead to several design-level recommendations. One main conclusion is that the Israeli power system already has the required resources to maintain frequency stability in case a large generation unit is lost. However, to maintain a reliable system, policy makers should encourage that the existing and additional storage will contribute to frequency regulation when there is a risk of instability. We also find that the location of renewable energy sources and energy storage systems has an impact on the frequency stability, and that it is better to place storage systems in the south, and renewable energy sources in the north. However, at least until 2025 this impact is not yet strong enough to be a leading factor in determining the location of these sources.

Coordinated Planning With Predetermined Renewable Energy Generation Targets Using Extended Two-Stage Robust Optimization

Coordinated planning is an effective method to balance investment costs and benefits in achieving high renewable target under the renewable-driven power system expansion wave. This paper proposes a coordinated planning model to support the efficient achievement of renewable target considering economy of the system by accounting for the interaction among source, grid, and energy storage system. An adaptive two-stage min-max-min robust optimization model is formulated to take into account renewable target as well as the uncertainty associated with renewable production and load demand. To reduce the conservatism of robust optimization, uncertain budget, multiple uncertain sets, and data-driven method are used to design uncertain sets. The resulting model is transformed into a tractable bi-level programming through strong duality theory and big-M method. A customized column-and-constraint generation algorithm is used to solve the bi-level programming. Simulation results presented for the modified IEEE 30-bus test system corroborates the effectiveness of the methodology, which finds siting and sizing of renewable energy sources and energy storage systems as well as transmission expansion schemes. It is capable to provide a flexible planning tool driven by renewable target under a reasonable computational burden.

Efficient RES Penetration under Optimal Distributed Generation Placement Approach

In this paper, a novel version of the Optimal Distributed Generation Placement (ODGP) problem regarding the siting and sizing of Renewable Energy Sources (RESs) units is presented, called Optimal RES placement (ORESP). Power losses constitute the objective function to be minimized, subject to operational constraints. The simultaneous installation of a mix of RESs is considered and the Capacity Factor (CF) ratio is used as an aid for taking into account: (a) the geographical characteristics of the area, in which the examined Distribution Network (DN) is placed, (b) the different weather conditions, and (c) the availability of RESs, all of that at the same time, while keeping the problem complexity at minimum. The contribution of this work is that the proposed methodology bypasses the weather uncertainties and, thus, the RESs’ power generation stochasticity and provides an adequate solution with minimum computational burden and time, since the proposed CF use allows solving the problem under a straightforward way. Unified Particle Swarm Optimization (uPSO) is used for solving ODGP and ORESP. Moreover, a sensitivity analysis regarding the CFs variations is performed and finally a comparison of the proposed method with a more realistic one is performed, to consolidate further the claims of this paper. The proposed method is evaluated on RES-region-modified 33- and 118 bus systems.

Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method

One of methods for loss reduction and reliability improvement of radial distribution system is using of renewable energy generation. In this paper, a new optimal placement and sizing of renewable energy sources based on photovoltaic panels (PVs) and wind turbines (WTs) in the distribution network is presented with the objective of loss reduction and reliability improvement based on energy not-supplied (ENS). A multi-objective evolutionary algorithm based on fuzzy decision-making method, called the Multi-Objective Hybrid Teaching–Learning Based Optimization-Grey Wolf Optimizer (MOHTLBOGWO) is proposed to solve the optimization problem. The proposed hybrid method has a high convergence speed and not trapped at all in local optimal. The proposed method is implemented in the form of single-objective and multi-objective on 33 and 69 bus IEEE radial distribution networks. The simulation results clear that the multi-objective optimization is a more precise approach to network utilization taking into account all objective indices than the single objective method. The results show that the proposed method has better convergence speed and less convergence tolerance in achieving to best solution in comparison with TLBO and GWO methods in loss reduction, reliability improvement and increasing the net saving and also in comparison with last studies. Moreover, the results show that dispersion of the size and location of distributed renewable generation leads to a further reduction in losses and a better improvement of the reliability criterion.

Sizing of renewable energy sources to support resilience in distribution networks

Microgrids may help researchers to analyze the performance of scaled electrical systems. The paper uses Unigrid to maximize the resilience of the unbalanced distributions network through optimal sizing and location of renewable energy sources. Unigrid is a scaled-down standard IEEE 13 microgrid built to analyze the behavior of electrical systems. The research methodology consist in evaluating the penetration of non-conventional renewable energy generation to the distribution network in different nodes and different injected power values. The first scenario is the connection of the microgrid with the power system of Providencia (Colombia), a low inertia network; the second scenario is the connection with La Guajira network, a high inertia included in the National Interconnected System (NIS). The connection of generators with non-conventional sources allowed to identify the percentage of non-conventional penetration sources of the modified IEEE 13 network in two scenarios in Colombia. The results lead that the candidate nodes to connect renewable sources are 632 and 671. In conclusion, the low inertia systems require a higher penetration than the high inertia system.

Operational scheduling of a smart distribution system considering electric vehicles parking lot: A bi-level approach

In this paper, a new bi-level framework is presented for operational scheduling of a smart distribution company (SDISCO) with electric vehicle (EV) parking lot (PL) and renewable energy sources (RES), i.e., wind and photovoltaic (PV) units. In the proposed bi-level model, maximization of the profit of SDISCO is obtained in the upper-level (leader) problem by minimizing the cost of power purchased from the wholesale market due to the EV PL unique capability, i.e., PL-to-grid. The lower-level (follower) problem aims to maximize the profit of the PL owner. This model is converted to a non-linear single-level problem by using Karush–Kuhn–Tucker (KKT) conditions. Fortuny-Amat and McCarl method is used for linearization based on auxiliary binary variables and sufficiently large constants. Moreover, uncertainties such as duration of the presence of EVs in PL, the initial state of the charge (SOC) of EVs and output power generation of wind and PV units are simultaneously considered through a set of scenarios. The SDISCO’s profit is investigated in four modes: (1) without RES and with the controlled charging of EVs; (2) without RES and with smart charging/discharging of EVs; (3) with RES and with the controlled charging of EVs; (4) with RES and with smart charging/discharging of EVs. In all these modes, a price-based demand response (DR) program is considered, as well as incentive-based DR, and combined price-based DR and incentive-based DR. The presented model is tested on the IEEE 15-bus distribution system over a 24-h period. The results show that SDISCO gains more profit by using a suitable charging/discharging schedule and employing a critical peak pricing (CPP) program. Furthermore, by comparing this bi-level model with the centralized model, the effectiveness of the bi-level model is demonstrated. Also, sensitivity analyses on the number of EVs, size of RES and the percentage of customer participation in the DR program are evaluated on the optimal operation of the SDISCO.

Inaugural Editorial

Inaugural Editorial

Optimal siting and sizing of renewable energy sources, storage devices, and reactive support devices to obtain a sustainable electrical distribution systems

This paper presents an integrated planning framework to optimally determine the location and allocation of renewable-based distributed generation (DG) units, energy storage systems (ESSs), and capacitor banks (CBs). This planning aim at improving the performance of electrical distribution systems (EDSs). In the proposed model, the cost of energy delivered by the substation and the investment costs are minimized. The environmental aspects are taken into account to obtain an efficient environmentally committed plan. The uncertainties due to PV generation and demand profile are considered via external uncertainty indexes in a deterministic environment. The proposed model is a mixed-integer nonlinear programming (MINLP) problem, which is recast to a mixed-integer linear programming (MILP) problem using appropriate linearization techniques. This approximated MILP model is implemented in the mathematical language AMPL, while the commercial solver CPLEX is used to obtain the global optimal solutions. The proposed model is validated by testing on a medium voltage distribution system with 135 nodes under different conditions and topologies.