Electricity Demand Management Research Articles

The dynamic model of Iran’s electrical energy supply system based on water-food-energy-climate change nexus

PurposeEnsuring energy security and controlling the share of energy in climate change are the two main challenges of the energy sector in the path of a sustainable future. This study has designed the dynamic model of Iran’s electrical energy supply system based on the water-food-energy-climate change nexus in order to identify sustainable policies for the supply of electrical energy resources and adaptation to Iran’s climate change process.Design/methodology/approachFor this purpose, first, the system dynamics model was designed with the participation of policymakers and the supply and demand data of water-food-energy resources and the trend of climate change and economic growth in Iran. After validation, the model is simulated in the 30-year horizon (2020–2050), and according to the results of the Monte Carlo sensitivity analysis, Iran’s electric energy supply policies are based on four strategies, including (1) Electric energy supply based on electric energy supply management from non-renewable sources, (2) Development of electrical energy supply based on management of energy supply from renewable sources, (3) Electrical energy supply based on electrical energy demand management and (4) Electrical energy supply based on adaptation to climate changes. By identifying and applying the policies of each strategy separately, the model was tested and the results were compared.FindingsBased on the implementation of the combination of selected policies in the model, the following policies have been proposed: 16% development of nuclear power plants, 18% reduction in the ratio of production of gas power plants to the total production of non-renewable power plants, and an increase in the production of combined cycle power plants through conversion of gas to combined cycle, energy aggregation and development of heat recovery systems in industrial units by 32%, a decrease of 5% Energy transmission and distribution losses, per capita reduction of energy consumption from 0.926 to the global average of 0.182 (MW) per year, management of water demand in the food sector by increasing irrigation efficiency to about 85%, a 27% increase in the area of land under the irrigation network, and reducing losses Food amounting to the global average of 0.9 m tons per year.Originality/valueThe proposed model is an application of system dynamics in the field of policymaking to ensure the security of electrical energy resources, taking into account the water-food-energy-climate changes nexus. The model is a valuable tool for policymakers in planning the sustainable management of resources in the path of adapting to climate change.

Improving the Structure of the Electricity Demand Response Aggregator Based on Holonic Approach

A demand response (DR) aggregator is a specialized entity designed to collaborate with electricity producers, facilitating the exchange of energy for numerous stakeholders. This application is a pivotal development within the Russian Energy System as it transitions to a Smart Grid. Its successful operation relies on the advancement and implementation of more efficient strategies to manage emerging energy assets and structures. The holonic approach is a distributed management model used to handle systems characterized by random and dynamic changes. This paper analyzes the specific aspects of the electricity demand management mechanism in Russia, primarily aimed at reducing peak load in the energy system by engaging active consumers who are outside the wholesale market. The DR-Aggregator is considered both a cyber-physical system (CPS) with a cluster structure and a business process. The DR-Aggregator exhibits essential holonic properties, enabling the application of a holonic approach to enhance the efficiency of the DR-Aggregator mechanism. This approach will facilitate greater flexibility in managing the load schedules of individual holon consumers, bolster the reliability of power supply by aligning load schedules among holon consumers within the super-holon cluster, and improve the fault tolerance of the DR-Aggregator structure, providing greater adaptability of demand management services.

Electrification as a factor in replacing hydrocarbon fuel

The article looks at the role of electrification that is expanding as a key factor in transitioning to a low-carbon economy. The study is based on a systemic approach and aims to provide a comprehensive understanding of the necessary transformations in energy production and consumption directed at phasing out fossil fuels and advancing the climate agenda. The novelty of the study resides in the development of conceptual foundations of the fuel-saving aspect of electrification which makes it possible to not only significantly reduce the negative environmental effects in regions, but also to ensure intensive economic growth through the application of advanced energy technologies and minimization of the use of a scarce energy carrier - natural gas - in the power industry and industry. The article clarifies the conceptual apparatus, defines the characteristics and patterns of the new stage of electrification, assesses the impact of electrification on the economic growth and environmental safety of the region, gives recommendations on optimizing the technological structure of the electric power industry in the context of the changing requirements for the parameters of electricity supply. The authors propose a methodology for accounting for the environmental factor in the electrification of the region, which includes ranking and selecting facilities to be electrified according to the minimum “electric fuel replacement ratio”; using the associated energy saving effect for financial compensation of the environmental burden, as well as introducing electricity demand management programs that improve the environmental situation in the region. The article defines the sequence of the implementation of the fuel-saving aspect of electrification and formulates a set of progressive solutions that would invigorate the relevant projects.

Management of a hybrid renewable energy storage system

The development of smart grids, made possible by advancements in computer technology, represents a transformative solution for electricity grids. These systems offer significant improvements in terms of security, reliability, and energy efficiency, while also promoting a low-carbon economy. Smart grids enable more efficient management of electricity supply and demand, as well as optimization of energy storage. However, despite these advantages, challenges persist, particularly in the areas of cybersecurity, and the reliability and speed of information processing. To address these challenges, we propose an innovative algorithm designed for energy management within electricity grids. This algorithm, specifically tailored for grids comprising hybrid storage systems, photovoltaic energy sources, and variable loads, aims primarily to balance supply and demand. This comprehensive approach not only ensures a continuous power supply but also reduces vulnerabilities associated with reliance on a single energy source. To evaluate the effectiveness of this algorithm, we conducted a series of rigorous simulation experiments using the MATLAB platform. These simulations meticulously tested the system's performance, providing substantial experimental evidence of its durability and viability in real-world scenarios. In summary, smart grids offer promising opportunities to enhance the security of electricity grids, maintain supply-demand balance, and promote sustainable energy storage practices. Despite persistent challenges in cybersecurity and information processing, our proposed algorithm represents a significant advancement in addressing these issues and strengthening the capabilities of smart grids.

Demand side management using ant colony optimization algorithm in renewable energy integrated smart grid

The increasing demand for electrical energy is a result of advancing technologies and changing lifestyles worldwide. Meeting this escalating energy need poses a substantial challenge, especially the difficulty in constructing new conventional power plants due to limited fossil fuel resources. To address this, demand-side management (DSM) in smart grid (SG), integrated with solar photovoltaic energy (SPE) have emerged as a crucial tool for effectively managing electricity demand, ensuring flexibility and reliability. DSM achieves optimal electricity utilization by rescheduling the operation schedules of consumer appliances and carefully adjusting their demand profiles. Integrating DSM into a smart grid framework is highly advantageous for the power industry’s pursuit of sustainable energy goals. While various heuristic-based optimization techniques have been employed for DSM, the focus on SPE has been limited to small-scale residential loads. This study utilizes the Ant Colony Optimization (ACO) algorithm to tackle a day ahead DSM minimization problem, considering SPE in areas with large number of appliances. The DSM minimization problem falls into the category of discrete combinatorial problems, making it well-suited for ACO optimization. The self-healing, self-protection, and self-organizing attributes of ACO make it particularly effective for DSM solutions. Residential, commercial, and industrial loads, with and without SPE integration, are considered to demonstrate the efficacy of the proposed ACO algorithm. Simulation results are compared with other studies in the literature, including Evolutionary Algorithm (EA), Moth Flame Optimization (MFO), and Bacterial Foraging Optimization (BFO), in terms of reducing consumer’s cost of energy (CCE) and utility peak load (UPL). The findings indicate that the proposed ACO algorithm outperforms the other algorithms considered in the current context.

Grid connected solar panel with battery energy storage system

A grid-connected battery energy storage system (BESS) is a crucial component in modern electrical grids that enables efficient management of electricity supply and demand. BESS consists of a set of batteries connected to the power grid, allowing for the storage and release of electricity when needed. This paper addresses the challenges associated with intermittent renewable energy sources and enhancing grid stability and reliability. The primary objective of this work is to store surplus electricity during low demand and supply it to the grid during peak demand periods or when renewable energy generation is low. By storing surplus energy, BESS helps balance supply and demand fluctuations, reducing the need for expensive fossil fuel-based power plants and minimizing greenhouse gas emissions. Additionally, BESS provides frequency regulation, voltage support, and grid stabilization. Furthermore, BESS reduces the intermittency of renewable energy sources like solar and wind, allowing for its integration into the grid. It allows the captured energy to be stored and utilized when the renewable sources are not actively generating electricity. Grid-connected BESS are a vital component in the transition towards a more sustainable and resilient energy future. They facilitate the effective utilization of renewable energy, enhance grid flexibility, and contribute to the reduction of carbon emissions, ultimately promoting a cleaner and more reliable electricity supply. The simulation of grid connected solar system with BESS is carried out using MATLAB/Simulink environment.

Learning-based demand-supply-coupled charging station location problem for electric vehicle demand management

We present a learning-based, demand-supply-coupled optimization model for the charging station location problem (CSLP), aiming to integrate the concept of electric vehicle (EV) charging demand management into the planning of charging infrastructures. In stage one, a gradient boosting-based learning model is developed to predict the charging demand of a charging station based on 15 defined features. Next, in stage two, a demand–supply-coupled CSLP model is developed to optimize the total charging usage rates of both existing and newly selected charging stations. We design a gradient-based stochastic spatial search algorithm to solve the proposed model. A case study with 6-year charging event data from Kansas City Missouri is performed. Results show that the proposed method can generate satisfactory charging demand predictions, and can increase charging usage rates by 14%, outperforming two benchmark approaches. The results of this research are poised to guide agencies in identifying optimal locations for new charging stations.

Energy scheduling in a smart energy hub system with hydrogen storage systems and electrical demand management

The energy price uncertainty in energy markets is the main challenge for achieving economic modeling of energy consumption and generation. This paper proposes optimal energy scheduling in a smart energy hub (SEH) under the uncertainty of electricity prices in the day-ahead. The proposed optimization approach is implemented by a two-layer interval approach for minimizing energy generation cost. In first layer optimization, demand side management (DSM) for electrical demand subject to the optimal level of electrical consumption is implemented. In second-layer optimization, the interval approach is used for modeling uncertainty. The energy generation cost is converted to bi-objective functions including average and deviation costs with conflicting nature than each other. Also, the multi-performance of the hydrogen storage system based on electrical and natural gas generation is considered for managing uncertainties in second layer optimization. Since the interval optimization approach is proposed in the second layer, minimizing the average cost and deviation cost are formulated as bi-objective functions by the augmented epsilon-constraint method. In the following, the LINMAP procedure is utilized to achieve maximum compatibility and the best trade-off consequence in the Pareto frontier of the multi-objective functions. Finally, the proposed optimization approach is modeled as a numerical simulation in the case studies to verify obtained results. The participation of the DSM and the hydrogen storage systems leads to minimizing the average and deviation costs by 3.03 % and 3.16 % in comparison with non-participation.

Peer-to-peer energy trading for demand response of residential smart electric storage water heaters

Environmental concerns and emission reduction targets are driving a transition from fossil fuel to renewable-based electricity generation. However, intermittent and distributed renewable generation brings challenges for the grid operation, as the low voltage distribution grid increasingly becomes constrained during high residential solar PV generation. The electrification of water heating presents a large opportunity to address this challenge by intelligently responding to electrical network conditions. Smart water heaters with thermal storage can soak up excess PV as a thermal battery, which facilitates maximum renewable generation and solves local grid problems. However, the centralized operation of electricity markets tends to impede the benefits of the localized distributed energy resources (DER). In such a scenario, peer-to-peer (P2P) energy trading allows neighbouring prosumers to trade energy between themselves with minimum interference from electricity grid operators. This research develops a P2P energy trading framework, using advanced, multi-zone electric storage water heaters with autonomous and aggregated control. The financial benefits identified in this research are forthcoming due to new participation in the electricity market ancillary services, demand management and P2P energy trading. Simulated results have identified that up to 92% of household water heating energy can originate from their own rooftop PV. At the electricity grid level, P2P energy trading demonstrates an increase in the aggregated PV self-consumption from 39% to 83%, importantly allowing the remaining 17% to target grid support in periods of supply shortfall. Average retail consumer energy savings of AUD$369/annum are identified, which include 17% of savings that are attributed to P2P energy transactions, delivering a capital payback of <3 years.

Integrating renewable energy sources for optimal demand-side management using decentralized multi-agent control

Demand-side management has garnered significant attention recently as a viable solution for electricity demand management. However, existing control approaches have encountered stability and energy efficiency issues that limit their effectiveness. To address these concerns, this work proposes a multi-agent control technique for optimal Demand-Side Management (DSM) in grid-connected mode. The proposed technique presents a novel multi-level nonlinear heterogeneous consensus control scheme to optimize the utilization of distributed energy sources and renewable energy resources under both fixed and dynamic communication scenarios. It validates the proposed model through extensive simulations and case studies conducted in MATLAB and JADE. Moreover, this work compares the performance of the proposed approach with state-of-the-art classical method, stochastic programming, model predictive control, and mixed linear programming techniques. The results of these techniques are evaluated and observe the superiority of the proposed approach. Decentralized coordination control results in energy savings of 21.4% through coordinated demand response schemes. Additionally, the proposed scheme surpasses other approaches in terms of active power sharing and delivering 14.2% more energy savings by effectively integrating energy storage sources and electric vehicles. Notably, the proposed approach achieve an impressive energy efficiency of 98.2% compared to 2% of other approaches. This remarkable efficiency translates to a 19% increase in energy storage capacity through multi-level coordination. This work further validates the practicality and feasibility of our proposed scheme to integrate distributed energy sources and renewable energy resources for sustainable and scalable energy models.

An incentivized scheme for electric vehicle charging demand management

The market share of electric vehicles (EVs) is expected to grow, necessitating infrastructure development to support this expansion. However, scientific literature predominantly focuses on charging infrastructure design, overlooking the importance of pricing strategies that can optimize their utilization. To address this gap, this study presents an integrated incentive scheme aimed at maximizing system utilization by effectively distributing the charging demand across stations. Moreover, a novel concept of Flex Accessible Charging Points (FLEX-ACP) is introduced, specifically designed to enhance accessibility and reduce waiting time for users with disabilities and seniors (UDS). The proposed framework utilizes an optimal charging network configuration, considering the number and locations of charging facilities, and then determines appropriate incentives at each station to minimize total waiting time. The problem is formulated as a bi-level optimization model, which is subsequently transformed into a single-level mixed-integer nonlinear program (MINLP). To solve this program, a combination of heuristic and branch-and-cut algorithms is employed. The developed models are tested through a realistic case study, utilizing real-world data from Salt Lake County in Utah.

Electricity peak shaving for commercial buildings using machine learning and vehicle to building (V2B) system

Reducing electricity peak demand is essential to maintain the balance between supply and demand side in electricity power markets, as well as reduce utility costs and environmental impacts. With growth in adoption of Electric Vehicles (EVs), there is an emerging opportunity to balance electrical power demand of buildings by storing electricity in EVs during low demand periods and discharging electricity into buildings during peak demand periods. Due to uncertainty in time and magnitude of peak demand, decision makers are always faced with a challenging task to identify optimal schedules for charging and discharging EVs to minimize peak electricity demand. This paper presents the development of a novel system that is capable of predicting day-ahead building electricity demand profile and identifying optimum schedule of charging and discharging EVs to minimize electricity peak demand. The system is designed to comply with planned EV trip schedules and minimum state of charge (SOC). The system consists of (1) machine Learning (ML) model to predict electrical power demand, and (2) demand management optimization model to identify optimal schedule for charging and discharging EVs. Four methods are explored to develop the ML model, including histogram-based gradient boosting, random forest, deep artificial neural network (DNN), and long short-term memory (LSTM). A case study of multi-tenant commercial building is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The results of the case study shows that LSTM has the best performance in terms of mean absolute error, root mean square error, and mean absolute percentage error with average values of 7.44, 17.78, and 20.08 %, respectively. Five scenarios for shaving peak electricity demand, including combinations of two electric vehicles, a stationary battery, and a PV system are investigated. Scenarios including the stationary battery and the PV system are considered to evaluate the full potential of peak demand reduction in the case study building. The results of the demand management optimization model show up to 36 % reduction in peak demand using two EVs, one stationary battery, and PV system of 40 kW capacity. The key contributions that this study adds to existing knowledge are: (1) developing machine learning models to predict day-ahead electricity demand in 15-minute intervals, (2) integrating machine learning and optimization algorithms in identifying EV charging and discharging schedules to minimize utility cost by shaving peak energy demand, and (3) considering planned EV trips and minimum SOC requirements in identifying optimal charging and discharging of EVS to shave peak energy demand in buildings. The implementation of this system provides practical solutions for managing electricity demand in commercial buildings using EVs. By reducing energy consumption and promoting the innovative use of EVs, this system offers a sustainable approach to managing electricity demand in commercial buildings.

Life-cycle assessment of current and future electricity supply addressing average and marginal hourly demand: An application to Italy

The increase in overall electricity demand in recent years, together with the rapid and significant changes in electricity generation has motivated increased research addressing environmental impacts of current and future electricity generation and supply systems. This article presents a comprehensive life-cycle assessment (LCA) of electricity generation and supply for the Italian current mix and future scenarios, for a wide set of environmental impacts and indicators, addressing intra-year variations, and including average and marginal demand impact perspectives. The generation mix was modelled for a 3-year period (2018–2020) with hourly and country-specific data, for the main generation technologies; and two future scenarios (for 2030) were modelled applying the EPLANopt energy system model. While future scenarios - with larger renewable energy shares - resulted in reduced environmental impacts in most categories, they were associated with burden shifts. In particular, increased shares of solar photovoltaic generation in future scenarios resulted in significant contributions to mineral and metal resource depletion and to land use impacts. The high penetration of renewable energy sources in future scenarios was also associated with important seasonal and hourly variations, demonstrating the importance of addressing intra-year temporal variations. Moreover, complementing an average with a marginal demand perspective provided insight on potential impacts of demand changes. When considering a marginal demand perspective, future scenarios offered no clear benefits in terms of global warming, for example. The research demonstrated the need for comprehensive and detailed environmental impact assessments with fine time resolution, which can assess seasonal and intra-daily variations, and the evaluation of environmental impacts with both average and marginal demand perspectives highlighted the complementary nature of the two in providing insightful results. The approach is fully replicable to other countries and regions - it can improve LCAs of electricity generation and supply and provide robust results to adequately inform decision-making on electricity generation and demand management, toward more sustainable energy systems.

Public preferences and increasing acceptance of time-varying electricity pricing for demand side management in South Korea

Linking demand and supply management on the basis of public acceptance is essential to prepare for the energy transition from fossil fuels to environment friendly energy integration, and to ensure stability in electricity supply and demand management. Demand management could promote the conservation of energy resources by stabilizing electricity supplies and curbing consumer consumption, which could consequently help reduce capital expenditures. Major developed countries have improved the efficiency of electric facility operations by implementing demand management based on pricing programs. The Korean government also seeks to improve demand management by introducing time-of-use (TOU) tariffs to residential electricity consumers. However, the rate of residential customers switching to the TOU tariff in Korea is still low, which negatively affects the success of demand management. Accordingly, this study aims to consider the preference structure of residential electricity consumers for TOU pricing using a discrete choice experiment, and further derive policy implications to promote the pricing program's switching rate. The analysis of consumer preferences in this study shows that consumers prefer a TOU tariff plan that minimizes uncertainty. Therefore, it is necessary to provide consumers with information on their consumption amounts and usage patterns based on real-time metering to reduce consumer uncertainties. In conjunction with the findings from the scenario analysis on the choice probability of TOU tariffs, incentives such as subsidies for purchasing high-efficiency appliances and carbon points are an effective measure to lower the barrier to switching, and reduce additional utility expenses. Furthermore, diversifying tariff plan attributes is required in subsequent studies on public acceptance to accurately identify consumer preferences while considering the TOU tariff plan's rate of diffusion.

Peak Electricity Demand Management and Energy Efficiency among Large Steel Manufacturing Firms in Nairobi Region, Kenya

Peak Electricity Demand Management and Energy Efficiency among Large Steel Manufacturing Firms in Nairobi Region, Kenya

Ценовые параметры поставки электроэнергии как базис управления спросом на электропотребление в регионе

Energy cost management (more than 45 % in the structure of total costs) can significantly reduce own energy supply costs and improve operational efficiency of an enterprise. The study examines regional electricity price parameters, affected by retail and wholesale electricity markets, in terms of the possibilities for industrial enterprises to implement price-dependent demand management to reduce energy purchase costs. The article aims to distribute Russian regions according to the prospects for reducing energy purchase costs by managing electricity demand schedules. The following methods were utilised: statistical analysis of hourly average electricity prices per year, month and day in the regional context; mathematical modelling and calculation of authors’ coefficients of average electricity prices and coefficients of daily price volatility for assessing prospects for effective energy demand management in Russian regions; construction of positioning maps for grouping and identifying regions where the implementation of price-dependent demand management is possible. Electricity price parameters of all Russian regions were examined. The paper analysed principles of electricity pricing for domestic industrial enterprises, researched the impact of price volatility on energy purchase costs, assessed the contribution of electricity costs to total energy consumption costs of enterprises. Coefficients of average electricity prices, coefficients of daily price volatility and coefficients of efficiency of price-dependent electricity consumption were applied to study electricity price parameters in the regional context. As a result, the article presented a map of electricity price parameters, where regions are grouped according to the possibility of implementing demand management mechanisms. Additionally, specific practical recommendations on price-dependent demand management for electricity consumption of industrial enterprises were given for each identified regional group.

Analysing the Residential Electricity Consumption using Smart Meter

A massive amount of electricity usage may be accessed on an everyday and hourly basis due to the advancement of smart power measuring technology. Electricity demand management and utility load management are made easier by energy usage forecasts. The majority of earlier studies have concentrated on the power consumption of business clients or residential buildings, or they have experimented with individual household electricity usage using behavioral and occupant sensor information. This study used smart meters to examine energy usage at a single household level to enhance residential energy services and gather knowledge for developing demand response strategies.The power usage of various appliances in a single household is estimated, by utilizing Autoregressive Integrated Moving Average (ARIMA) modeling technique, which is applied to daily, weekly, and monthly information granularity. To select the household’s energy consumption dataset for this study, a multivariate time-series dataset describing the four-year electricity usage of a household is provided. The use of Exploratory Data Analysis (EDA) is utilizedfor the selection of features and data visualization. The correlation coefficients with the daily usage of the household have been computed for the characteristics prepared for the forecast. The top three major determinants with the top three positive significance are "temperature," "hour of the day," and "peak index." A single household's usage is inversely related to the variables having negative coefficients. It should be noticed that the correlations among a household's attributes with usage vary from one another. Finally, the power prediction is analyzed in a single household.

Assessment of the Electric Demand Management Potential of Educational Buildings’ Mechanical Ventilation Systems

Demand management is expected to reduce emissions from energy systems and support the utilization of renewable energy sources. In this paper, the focus is on the viability of educational buildings’ mechanical ventilation systems’ participation in electric demand management. The results suggest that when load shedding lasts for a short duration, the ventilation machine load seems more promising than expected for electric demand management, as even 60% of its electric power could be granted to such markets. Prolonging the load-shedding duration increases the risk of the indoor carbon dioxide (CO2) concentration exceeding the limit for good indoor air quality. This paper contributes to the academic community by providing information for the assessment of the demand management potential of buildings and eventually their significance in decarbonizing the electric energy system and filling research gaps concerning the impact of implementing demand management that involves a reduction in ventilation rate.

Electrification of residential heating, cooling and hot water: Load smoothing using onsite photovoltaics, heat pump and thermal batteries

Due to the pressing timeframe to meet emissions reduction targets, electrification of heat is the fastest approach for decarbonising buildings. However, the high power demand of electric heating and cooling devices poses a significant challenge to the electricity supply chain when they are deployed on mass. Adding rooftop solar photovoltaic systems does not necessarily eliminate this problem. Cheap and safe thermal storage offers a potentially viable solution allowing electric HVAC & hot water systems to be deployed with minimal impact on the grid. We propose such a multi-function thermal system combining solar photovoltaics, heat pump, and thermal storage for supplying residential heating, cooling and hot water demand. We investigate the effectiveness of this system in terms of electricity demand management and solar utilisation in different geographical locations. The results illustrate that with effective control, such a system can reduce a building’s annual grid-electricity demand by about 50% to 80%, and increase its solar self-consumption to around 60%. The temporal load profile is also significantly reduced and flattened, allowing high level of distributed PV penetration without negative impacts on the grid. We show that these benefits can be earned at a reasonable cost to consumers helping them to reap a positive rate of return compared to alternative options for different climatic and regional conditions. The proposed thermal storage system is demonstrated as a cost-effective and safe distributed energy solution to ease the stress exerted on the grid from excess PV power as well as heating and cooling related peak demand.

A Multi-Objective Fuzzy Optimization Model for Electricity Generation and Consumption Management in a Micro Smart Grid

This manuscript proposes an intelligent supply and demand management system in a complete network of electricity production and consumption. A micro smart grid (MSG), which includes a solar cell, a wind turbine, a diesel generator, and battery storage system capable of trading energy with the smart gride (SG), connected to smart buildings with different types of loads is modelled. Different types of intelligent fuzzy controllers for distributed management were proposed and optimized via the non-dominated sorting genetic algorithm-II (NSGAII), which is a multi-objective optimization method. Maximum user comfort, the amount of renewable energy employment, minimum total power consumption cost, total energy consumption at peak time, and MSG loss of power supply probability are the five objective functions of the optimization process. Various uncertainties of the real world have also been considered. The most crucial distinguishing feature of this proposed method is the design of controllers to manage the demand and supply of electricity without the need for daily optimization. Comparison experiments with other methods presented in the field of electricity supply and demand management are conducted to show the superiority of this method in terms of optimality of the results, low processing volume required to implement real controllers, and its resilience to changing conditions.