Demand-side Management Programs Research Articles

Optimizing power systems and microgrids: A novel multi-objective model for energy hubs with innovative algorithmic optimization

This article delves into the dynamics of power system and microgrid planning, emphasizing the emerging significance of energy hubs. The study introduces a pioneering multi-objective model that integrates economic considerations and microgrid pollution mitigation. The focus is on renewable sources such as wind and solar, coupled with advanced energy storage systems like E-fuel. Sophisticated probabilistic modeling techniques address uncertainties in renewable resources, complemented by a demand-side management program for cost reduction during peak hours. Transforming into an optimization problem, an innovative algorithm inspired by African vulture foraging behaviors provides a distinctive approach to energy landscape optimization. In a comparative analysis with established models, this approach demonstrates proficiency in simulating market behavior and providing optimal strategies for profit maximization. In a simulation analysis of four scenarios, the impact of risk assessment, a novel electronic fuel storage market, and an innovative energy storage solution on operational costs and pollution emissions is scrutinized. Notably, the proposed method excels in Case 4, showcasing a significant reduction in both operational costs and emissions. Case 4 demonstrates a substantial reduction in operational costs to $1745.732 and pollutant releases to 10753.621 kg, outperforming existing models with lower operational costs and pollutant releases.

Auditing and Analysis of Natural Gas Consumptions in Small- and Medium-Sized Industrial Facilities in the Greater Toronto Area for Energy Conservation Opportunities

This paper presents the findings of fifteen energy audits conducted on industrial sites in Canada’s Greater Toronto Area (GTA). The audits covered a range of industries including food processing, packaged goods, and finishing processes (powder-coating). The primary focus of the audits was to analyze the natural gas consumption and the performance of major-gas-consuming equipment. The audits identified natural-gas-consuming equipment that could be optimized to yield energy and operational cost savings and greenhouse gas (GHG) reduction opportunities. Food production plants’ energy intensity ranges from 5.59 m3/ft2 to 17.73 m3/ft2. Therefore, there is a significant opportunity to improve energy consumption through better technology integration. The results of the audits indicate a trend of an increase in the percentage of non-productive consumption with non-productive time. The proposed energy-saving measures include reducing non-productive natural gas consumption, gas-fired equipment tune-up, optimizing boiler loads, and reducing oven exhaust by using variable frequency drives (VFDs). The findings of this study could be used to develop a demand-side management program specifically for small- and medium-sized industrial facilities in the Greater Toronto Area and other parts of Canada.

How demand-side management can shape electricity generation capacity planning

Forward-looking strategies for Demand Side Management (DSM) can accelerate the clean energy transition and reduce carbon emissions if electricity load can be shaped to be better matched to the hourly availability of renewable resources. Time-varying rates are increasingly used to motivate load shifting, capitalizing on the increasing size of loads that can be easily shifted, such as electric vehicle charging and electric heat pumps. This study analyzes the hourly availability of current and renewable resources for two representative electricity grids in the USA. While many demand-management programs are currently designed to shift load to nighttime when demand is low, to use low-cost PV the opposite shift into daytime will be preferred. Given that investments in electricity generation capacity shape the grid for decades, generation capacity planning should proactively anticipate how DSM programs (and associated load shapes) will change as the generation mix changes. These future load shapes determine the optimal resources to be acquired and can be modified by DSM to facilitate variable renewable integration. Ongoing regulatory proceedings in Colorado are described as a case study for an existing decision-making process.

An enhanced coati optimization algorithm for optimizing energy management in smart grids for home appliances

This research presents an innovative approach to energy management in smart homes, aiming to efficiently regulate energy demands while ensuring customer loyalty. The focus is on addressing the limitations of existing demand-side management (DSM) programs, which predominantly target the residential sector. The proposed solution introduces the Adaptive Coati Optimization algorithm, which optimizes device organization based on Critical-Peak-Price and Real-Time-Price power payment systems. By strategically managing energy consumption, the algorithm reduces electrical expenses and peaks without compromising user convenience. The study evaluates the effectiveness of the approach across three operational periods (60 minutes, 12 minutes, and 24 minutes) to align with varying customer needs. Overall, the Adaptive Coati Optimization algorithm offers a promising solution for cost-efficient energy management in smart homes, combining both financial benefits and enhanced user satisfaction. The results indicate a significant decrease in tariffs rates, up to 30%, leading to a 20% increase in user satisfaction and a 25% improvement in cost utilization.

The role of social learning on consumers’ willingness to engage in demand-side management: An agent-based modelling approach

Achieving a sustainable energy future requires a clean, affordable energy supply and active consumer engagement in the energy market. This study proposes to evaluate and simulate energy consumers' willingness to participate in demand-side management programs using an agent-based modelling approach to address the social learning effect as a key factor influencing energy consumer behaviour. The proposed agent-based model simulates households' electricity consumer interactions examining how the willingness to shift electricity usage is encouraged through the social environment, while accounting for the diversity among consumers. Data from a survey conducted in Portugal, including questions about the influence of recommendations from friends or family members on individuals' willingness to engage in demand response activities, are used to test the proposed simulation. The findings reveal that social learning significantly impacts demand response acceptance, yet the extent of this influence varies depending on the socio-economic characteristics of households’ electricity consumers. The study confirms agent-based model as an effective approach for capturing social dynamics and supporting electricity market decision making, providing valuable insights for devising consumers engagement strategies.

Smart grid optimization considering decentralized power distribution and demand side management

AbstractThe increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighbouring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%.

Multi-objective scheduling and optimization for smart energy systems with energy hubs and microgrids

This paper introduces a novel model for optimizing microgrid systems by integrating multi-purpose renewable energy (MEM) and cutting-edge technologies, including electric vehicles (EVs). The proposed MEM demand response programs encompass various energy sources such as wind energy, multi-carrier energy storage technologies, boilers, combined heat and cooling units, EVs, P2G (power-to-gas), and demand response capabilities. The primary objective is to minimize the total operational cost of the microgrid system. A distinctive aspect of the proposed method lies in considering the prices of all energy carriers as unknown variables. Market prices are integrated into the modeling process, incorporating scenarios with reasonable probabilities and taking into account demand-side management programs. Moreover, the model allows for customizable programming of different parts of the multi-energy microgrids, with a focus on maintaining convexity principles for the operating area of each CHP (combined heat and power) unit. To tackle the unique complexity of this optimization problem, a developed blue whale optimization algorithm is proposed. This algorithm builds upon the main whale optimization algorithm (WOA), a promising population-based optimization approach. However, the effectiveness of WOA heavily relies on the careful setting of exploration and exploitation parameters, which may lead to being trapped in local optima. To address this challenge, the paper introduces a self-adaptation modification based on wavelet theory to enhance the WOA's performance. The proposed model and optimization method are thoroughly evaluated through simulation studies for different scenarios, showcasing their efficacy in achieving cost-efficient and sustainable microgrid operation. The combination of MEM and emerging technologies like EVs, along with the improved optimization algorithm, marks a significant contribution to advancing the planning and operation of microgrid systems towards low-cost, reliable, and environmentally-friendly energy solutions.

Multi-objective optimization based demand response program with network aware peer-to-peer energy sharing

Demand-side energy management programs, such as Demand Response (DR) and Peer-to-Peer (P2P) energy sharing, have recently begun to be implemented to optimize the integration of distributed clean energy resources and to leverage generation and demand-side flexibility in the distribution system. In general, if not optimally planned, P2P energy sharing may have a negative impact on power distribution utilities’ primary objectives, such as minimizing energy losses, flattening the load profile, and improving voltage regulation. The DR program is considered as one of the potential mechanisms to alleviate the possible adverse impact of P2P energy sharing while maintaining the interest of peers participating in decentralized energy sharing. The analysis of the impact of P2P energy sharing with varying peers’ physical locations involved utilizing the Voltage Sensitivity Factor (VSF). The influence on the distribution network is contingent upon the difference in VSF values among the individual peers engaged in energy sharing. This study introduced the k-Means algorithm for VSF-based peer clustering, thereby generating the merit order and realizing the network-aware P2P energy transactions for the selected scenarios. A multi-objective optimization model-based real-time pricing-based DR scheme has been formulated to address the adverse impact of P2P energy sharing. Furthermore, this study quantifies the impact of DR potentials, which vary from 5% to 45% in increments of 10%, on the Peak-to-Average Ratio (PAR), overall energy loss, and total energy cost reduction. The modified IEEE 33 bus system incorporating industrial, commercial, and residential consumers’ load profile of 24 h at selected buses has been considered to test and validate the proposition.

Flexible demand-side management program in accordance with the consumers’ requested constraints

Increasing electrical energy consumption during peak hours leads to increased electrical energy losses and the spread of environmental pollution. For this reason, demand-side management programs have been introduced to reduce consumption during peak hours. The most important challenge of these programs is the low participation of consumers due to their special constraints. Hence, presenting a flexible demand-side management program that optimizes the consumption pattern of consumers in accordance with their requested constraints will have a significant impact on further participation of consumers in reducing the peak consumption. In this paper, a flexible demand-side management program is presented that provides the optimal consumption patterns, by using the stair-step pricing mechanism and considering the consumers requested constraints. The proposed method is carried out for a combination of consumers with various requested constraints such as the lowest and highest possible consumption per hour and the stability of the total consumption of each consumer in a certain period of time. The simulation outcome showed that the proposed algorithm is able to encourage consumers to participate more in energy management program by meeting their various requested constraints. Peak load total consumption experienced a decline of 25.09% from 36649.4 to 27454.3 kW-hours. As a result, the daily energy cost had been reduced by 10.27 % from 13854.466 to 12,431 dollars. This cost reduction was derived from reducing the need for additional energy generation during peak hours due to transferring consumptions to the lower-priced hours.

Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy and demand-side management

This study presents the Enhanced Cheetah Optimizer Algorithm (ECOA) designed to tackle the intricate real-world challenges of dynamic economic dispatch (DED). These complexities encompass demand-side management (DSM), integration of non-conventional energy sources, and the utilization of pumped-storage hydroelectric units. Acknowledging the variability of solar and wind energy sources and the existence of a pumped-storage hydroelectric system, this study integrates a solar-wind-thermal energy system. The DSM program not only enhances power grid security but also lowers operational costs. The research addresses the DED problem with and without DSM implementation to analyze its impact. Demonstrating effectiveness on two test systems, the suggested method's efficacy is showcased. The recommended method's simulation results have been compared to those obtained using Cheetah Optimizer Algorithm (COA) and Grey Wolf Optimizer. The optimization results indicate that, for both the 10-unit and 20-unit systems, the proposed ECOA algorithm achieves savings of 0.24% and 0.43%, respectively, in operation costs when Dynamic Economic Dispatch is conducted with Demand-Side Management (DSM). This underscores the advantageous capability of DSM in minimizing costs and enhancing the economic efficiency of the power systems. Our ECOA has greater adaptability and reliability, making it a promising solution for addressing multi-objective energy management difficulties within microgrids, particularly when demand response mechanisms are incorporated. Furthermore, the suggested ECOA has the ability to elucidate the multi-objective dynamic optimal power flow problem in IEEE standard test systems, particularly when electric vehicles and renewable energy sources are integrated.

A multi‐layer–multi‐player game model in electricity market

AbstractHere, a novel tri‐level energy market model aimed at addressing the challenges posed by demand side management (DSM) in the electricity distribution company (EDC) is introduced. DSM has emerged as a new strategy employed by EDCs to manage and control electricity demand by encouraging end‐users to modify their electricity consumption patterns. This is achieved through the participation of demand response (DR) aggregators, which play a crucial role in assisting end‐users with strategies and technologies to reduce their electricity consumption during peak hours. The proposed tri‐level energy market model consists of four distinct players: EDC, microgrids, aggregators, customers. The interactions between these four actors are modelled within a tri‐level game framework, where the EDC and aggregators act as leaders, and the micro‐grids and customers are followers. This multi‐level and multi‐player game structure allows for a more realistic representation of the complexities involved in DSM programs within the energy market. To demonstrate the effectiveness of the proposed model, a real case study is utilized, showing that the new model better resembles real‐life market conditions. The results illustrate how the tri‐level energy market model can significantly reduce demand fluctuations during peak hours, leading to improved efficiency and effectiveness within DSM programs.

IoT-enabled Smart Energy Management Device for Optimal Scheduling of Distributed Energy Resources

In this paper, a robust Internet-of-Thing (IoT) enabled Smart Energy Management Device (SEMD) is developed for Distributed Energy Resources (DERs) equipped energy consumers. The SEMD communicates with the existing metering infrastructure through serial communication using the Modbus RTU protocol and the RS 485 communication standard. The SEMD’s energy management framework comprises three sequentially operated software modules, viz., data preprocessing, forecasting, and an optimization module, which aims to minimize the energy costs of end consumers. The software and hardware architecture of the developed SEMD is robust enough to fit real-time implementation and makes it user-friendly. The effectiveness of the SEMD has been tested on an experimental test rig at the Indian Institute of Technology Gandhinagar. The demonstration test rig comprises two DERs equipped energy consumers (viz. Energy consumer-A & B). Energy consumer-A comprises a roof-top solar PV system, Battery Energy Storage System (BESS), and conventional flexible/non-flexible household appliances. The Energy consumer-B consists of Vehicle-to-Grid (V2G) enabled Electric Vehicle (EV), lighting, and pump loads. The experimental results obtained in this work demonstrate that using SEMD satisfies all technical constraints and benefits consumers in reducing bill payments. Thus, SEMD is a critical enabler for DER-equipped energy consumers to participate actively in various demand-side energy management programs.

Smart Demand Side Management for Techno-Economic Analysis of Microgrid System

The world uses about 20 trillion kilowatt hours of electricity annually. Because of this great level of necessity, the use of green energy is growing and is becoming more and more significant in today's society. Demand-side management (DSM) programs are described as the procedures for planning, carrying out, and overseeing the tasks assigned by electric utilities to encourage customers to adjust their loads and energy usage in order to reduce power consumption and lower their electricity costs. It is utilized in smart grid systems to balance energy generation and consumption in a way that benefits both customers and utilities, to achieve the goals of utility energy policies, and to modify the quantity and quality of power and electricity used. Customers should have the capacity to control their energy consumption, especially during peak usage, to enable them to make better informed decisions. Demand response (DR) and energy efficiency techniques were employed by DSM to assist energy users in lowering their consumption. to increase resilience, economy, sustainability, and efficiency. The main objective of this study is to apply two demand side management strategies, examine power flow analysis on distribution networks utilizing IEEE rules both before and after PV cells are integrated, and assess the impact of doing so. The approaches' practicality is confirmed by comparing the simulation results from one technique with the other. For the end user, lowering their electricity cost is their primary goal.

Using neural networks to forecast marginal emissions factors: A CAISO case study

Marginal Emissions Factors (MEFs) quantify the time-dependent changes in CO2 emissions resulting from changes in electricity consumption. Accurate MEFs are critical for calculating the emissions impact of demand-side management (DSM) activities and programs, but current methods of calculating MEFs are limited by their temporal resolution, accuracy (particularly in grids with high penetrations of variable renewable energy), and ability to predict MEFs ahead of time, reducing their utility for DSM. We improve upon existing techniques by introducing a novel multi-layer perceptron to linear composite model that uses publicly available grid data to calculate historical MEFs and predict day-ahead MEFs. We test our model on publicly-available data from the California Independent System Operator over the period of 2019–2021, a grid with high daytime VRE generation. Results indicate that our model produces more accurate and more granular demand-based MEF estimations than comparable regression techniques and maintains high accuracy when use to forecast future MEFs. Our MEF framework can be applied to other regional grids to evaluate and design DSM strategies that leverage CO2 emissions-reductions as motivation for altering electricity consuming behaviors.

Demand response programs: Comparing price signals and direct load control

Canada's electric power system is responsible for approximately 9 % of national emissions, 53 % of which occur in the province of Alberta. Integrating new variable renewable energy resources is a key part of the supply-side decarbonization pathway, while end-use electrification unlocks further opportunities on the demand side. The inherent variability of variable renewable energy output necessitates network flexibility. Supply-side flexibility solutions require significant investment, but demand-side management programs have potential to deliver network flexibility at a lower cost. An integrated framework consisting of demand and supply models investigates the efficacy of demand response programs for improving network flexibility in Alberta's power system, as measured by multiple operational metrics. The framework is applied to two demand response programs: real-time pricing and direct load control. These programs are assessed for two generation capacity mixes derived from an expansion planning model. Results indicate that substantial network flexibility benefits are achievable in a zero-emission power system via both programs resulting in avoided wind curtailment by 7.7 %. Improvement in operational costs, up to 1.4 % of the base output, is also observed in all scenarios accompanied by savings in household power bill expenditure by up to 15 %. These improvements, however, are only achieved fully when sufficient flexible load is provided.

Optimization-based optimal energy management system for smart home in smart grid

The introduction of advanced technologies has led to an unprecedented rise in automated appliances in the housing sector. Building new administrative structures to satisfy electrical needs has grown more crucial to ensure the safety of residential devices. One of the approaches to achieve this is Demand Side Management (DSM), a key component of both micro-grid and Smart Grid technology. DSM can be accomplished by carefully controlling requirements while upholding the trust of clients. Most of the DS Management which has been covered in the research is aimed at helping households manage their power plan. The innovative HBA+DMO technique inherits Honey Badger Optimization (HBA) and Dwarf Mongoose Optimization (DMO) for executing the DSM program. The groundwork for the proposed framework implemented in this investigation is provided by the Critical-Peak-Price (CPP) and Real-Time-Price (RTP) payment processes. Two operational instances (60 min and 12 min) are being taken into consideration to evaluate client requirements and behavior over the suggested strategy. In accordance with the results from simulations, the suggested strategy arranges the devices in the best possible way, leading to fewer energy expenses while maintaining user comfort (UC). Customers sometimes pay a premium as a result of gadget waiting periods in order to gain the most comfort. As equipment is turned on in response to user comfort, the amount of time spent waiting during an unscheduled situation is close to zero. Tools for lowering energy expenditures and consumption for buildings, communities, or enterprises are frequently provided through energy management software. The three main uses of the energy data that EMS collects are reporting, monitoring, and engagement. The computational time of the proposed approach is (∼213.42). Future testing involving different conditions and control methods for study into HRES microgrid infrastructure may be done on the medium of the experiment bench.

A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment

The increasing population migration to urban and peri-urban areas increases basic service needs for cities worldwide. Residential electricity demand increases with more customers and varies with novel uses, such as charging electric vehicles, which may add additional dynamics to the residential electricity demand profile. Widespread installation of smart electricity meters provides fine temporal resolution data reflecting current demands and supports predicting future demands. As part of a demand-side management program, understanding the main drivers of current electricity usage based on demand-driven and exogenous predictors represents a valuable tool for utilities facing new demand scenarios. This work presents the application of multiple models to forecast electricity demand based on the input data and the forecasting horizon. Models with exogenous variables as predictors are part of the input–output category, including a Feed Forward Neural Network, Random Forest, and a Linear Gaussian State Space model. The second category is demand-driven models, where predictors include only previous demand values. The demand-driven models in our analysis include a univariate Nonlinear Autoregressive Neural Network and a Linear Gaussian State Space. Using smart electricity meter data from the greater Chicago area, we compare the performance of the models on two different types of accounts: single- and multi-family residential users when forecasting one and multiple steps. Results show that the Linear Gaussian model reports an R2 of 0.99 compared to an average R2 of 0.92 from the Feed Forward Neural Networks and Random Forest when forecasting single- and multi-family electricity demand one step ahead. However, Nonlinear Autoregressive Neural Networks report an average R2 of 0.85 compared to the Linear Gaussian R2 of 0.58 when forecasting 48 steps. We also found that the most important predictors for single-family demand are temporal variables like weekdays, working and non-working days, and day hours. For multi-family demand, electricity demand at the same hour as the previous week replaces weekdays as a significant predictor. Different forecasting models can assist utilities and city planners to manage demand under different and novel residential electricity usage conditions.

Fast in the pandemic, durable after droughts, inequal during economic downturn. A 20 year multi-dimensional retrospective analysis of water demand change in Southern California

Determining changes in water consumption behavior due to recurring external stressors (e.g. droughts and socio-economic fluctuations) and previously unseen shocks like the COVID-19 pandemic can support future demand-side management (DSM) programs and identifying vulnerable social groups. Yet, most studies analyzing external drivers of water consumption change are noncomparative as they examine only one stressor at the time, mainly droughts. Here we develop a multi-dimensional retrospective analysis (MDRA) to quantify residential water consumption changes in Southern California over the last two decades under compound hydroclimatic, economic, and pandemic stressors. We then uncover what socio-economic groups relate to predominant behavioral changes under each stressor. We observe a long-lasting decreasing water consumption trend for more than 76% of the analyzed households. Socio-economically vulnerable households were more likely to reduce water consumption during the economic and pandemic stressors. Differently, affluent neighborhoods tended to increase water consumption during or after droughts and the pandemic. Our MDRA supports utilities and policy makers to discern the impacts of different stressors on water demand and helps to identify social and economic sensitivities across a service area to develop adaptive DSMs and assistance programs.

Optimal stochastic scheduling in residential micro energy grids considering pumped-storage unit and demand response

Improving energy efficiency, production costs, greenhouse gas emissions, and reliability are some of the most important problems in the energy industry. Energy storage systems and demand-side management (DSM) programs are among the existing solutions for removing such problems. This study presents an improved micro energy grid (MEG) to reduce operating costs as well as greenhouse gas emissions using combined cooling, heating, and power (CCHP) systems, wind turbines (WTs), photovoltaic (PV) units, pumped-storage units, and heating and cooling storage units. In this system, pumped-storage units are employed to store energy. Price- and incentive-based programs are employed to obtain better performance on the demand side. Uncertainties of wind speed, solar power, electricity price, and electrical load are considered to achieve more accurate results. In this paper, the Latin hypercube sampling (LHS) method is applied to generate various scenarios. Then, K-means clustering approach is employed to reduce the number of scenarios. By producing and reducing the proposed scenario, the inherent uncertainty in renewables can be covered, which will result in a more efficient model. Meta-heuristic whale optimization algorithm (WOA) and Pareto model are used to solve single- and multi-objective functions and to optimally design the proposed structure considering contradictory functions, respectively. The features of the proposed developed algorithm are simplicity in execution and absence of derivative operator, increase of local and global search capability and consideration of objective functions with different nature. Moreover, fuzzy decision-making mechanism is proposed to select the optimal solution from the Pareto set of solutions. Finally, the proposed approach in different scenarios on the microgrid with various sources is discussed. The results indicate the proposed method properly obtains the optimal solution.

Smart distribution grid management during a worst-case scenario of renewable energy outage

Nowadays, due to the high penetration of renewable energy sources such as wind generation and PV in the distribution network and the possibility of their outage, the optimal operation of the distribution grid has faced challenges. In this paper, a bi-level model based on mixed integer quadratic programming (MIQP) is proposed for the optimal operation of smart distribution networks under the worst case of the outage of renewable energy resources. At the upper-level problem, minimization of the energy losses, energy purchase, and load shedding in the demand side management program is formulated, and at the lower-level problem, the maximization of output and curtailment of renewable energy resources is modeled. At the lower level, the worst-case realization of the renewable outage is derived and at the upper level, optimal operation of the distribution network is done under the worst-case realization of renewable resources. The reformulation method based on Karush–Kuhn–Tucker (KKT) conditions is considered to solve the proposed bi-level model, which is faster and less complicated than similar algorithms. The IEEE 33-bus distribution grid is considered for the analysis of the proposed model and method, which proves the accuracy and optimal performance of the proposed model and method. The findings indicate that, with the implementation of the suggested model, the smart distribution network successfully avoided load-shedding even when various renewable resources were disconnected. Also, the proposed model in operation under normal conditions has caused a 56% reduction in losses and a 53% reduction in energy purchases compared to the outage of 6 renewable units in the distribution network.