Demand-side Management Programs Research Articles

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.

Load profiling of commercial and residential building using clustering technique

Data mining is a promising tool used in processing energy data collected from energy consumers. The knowledge derived from energy data is very pertinent in the formulation of various demand-side management programs. This paper uses clustering techniques to segment the energy consumption patterns of residential and commercial buildings; situated at different geographical locations. The two (2) commonly used clustering techniques: K-Means and Agglomerative Hierarchical Clustering, were employed. The result indicates that the choice of clustering technique for load profiling is highly subjective to the nature of the dataset. Hence, using Davies-Bouldin Index (DB) Index and Silhouette Index (SI) as clustering indicators to select an optimum number of clusters and the best clustering technique. Hierarchical clustering was identified as the most appropriate clustering for the two buildings.

Performance enhancement of smart grid with demand side management program contemplating the effect of uncertainty of renewable energy sources

ABSTRACT In a day ahead electricity market, all candidates of the electricity market, i.e. electricity users, aggregator, and grid operator urge to grow individual profit, but it is quite challenging to assure profit for all the candidates at a time. In this work, a multi-objective problem is formulated by combining the concept of demand side management (DSM) and Dynamic Economic Emission Dispatch (DEED). The multi-objective DSM – DEED problem is optimized by class topper optimization algorithm. In addition, an energy management algorithm (EMA) is proposed for optimal power utilization from various energy sources and to match the load demand with generated energy in presence of uncertainties of RESs. To get an accurate model, a random forest regression-based machine learning approach is considered in this paper to predict load demand, wind, and solar power on an hourly basis for a span of 24 hours. The objective here is to optimally schedule load consumption and power generation patterns simultaneously for a day to improve load factor, minimize operational cost, and maximize the profit of all the candidates of the electricity market simultaneously. The simulation findings highlight the effects of the proposed EMA and modified DSM program on the smart grid’s economy and performance.

Loads scheduling for demand response in energy communities

This paper focuses on optimizing the collective self-consumption rate in energy communities by scheduling members’ loads. The community remains connected to the public grid and comprises prosumers, traditional consumers, and distributed storage units. Prosumers can exchange their energy with the public grid or other members. The proposed strategy aims at implementing a Demand Side Management program taking advantage of controllable loads’ characteristics. A MILP formulation of the problem allows, on the one hand, to give the optimal planning for electrical devices’ operations. On the other hand, it provides optimal solutions for managing the storage units, peer-to-peer exchanges, and interactions with the public grid to minimize the energy flows from the public grid over time. However, this MILP only allows for solving small problem instances. Thus, we develop a column generation-based heuristic for large problem instances. Our numerical experiments based on real data collected in the south of France show that joining an energy community saves money on energy bills and reduces the total energy drawn from the primary grid by at least 15%.

The Innovative Research on Sustainable Microgrid Artwork Design Based on Regression Analysis and Multi-Objective Optimization

One of the most vital issues in electrical systems involves optimally operating microgrids (MGs) using demand-side management (DSM). A DSM program lowers utility operational costs in one sense but also needs policies that encourage financial incentives in the other. The present study formulates the optimum functioning of MGs using DSM in the form of a problem of optimization. DSM considers load shifting to be a viable option. There are operational limitations and executive limitations that affect the problem, and its objective function aims at minimizing the overall operational prices of the grid and the load-shifting prices. The major problem has been solved using an improved butterfly optimization scheme. Furthermore, the suggested technique was tested in various case studies that consider types of generation unit, load types, unit uncertainties, grid sharing, and energy costs. A comparison was made between the suggested scheme and various algorithms on the IEEE 33-bus network to demonstrate the proficiency of the suggested scheme, showing that it lowered prices by 57%.

IoT-based intelligent energy management system for optimal planning of HVAC devices in net-zero emissions PV-battery building considering demand compliance

To achieve the goal of carbon neutrality, renewable energy resources are now being widely deployed in the building sector, forming the hybrid energy system. For such hybrid energy system, the development of energy management system (EMS) is of great significance for their maximum utilization. In this study, an Internet of things-based (IoT-based) intelligent EMS is developed for a real net-zero emissions photovoltaic-battery (PV-battery) building, where its main purpose is to optimally schedule the heating, ventilation, and air conditioning (HVAC) devices in order to tradeoff between user comfort level and battery remaining energy. The developed EMS mainly consists of two modules, including data-driven forecasting module and mixed-integer programming module. The data-driven forecasting module is firstly developed to forecast future 24-h PV generation, baseload demands, and room thermal dynamic using several data-driven methods. Then, based on the forecast results, the optimal planning problem of HVAC devices is formulated as a multi-objective optimization problem, which is solved by the mixed-integer programming module. Moreover, a novel concept named as demand compliance is proposed, where the user-defined demand important coefficient is considered in the optimization problem. Comprehensive case studies are conducted to compare the developed EMS and existing rule-based control method. The experimental results demonstrated that the developed EMS could achieve similar user comfort level comparing with rule-based method, while its battery remaining energy was significantly higher. This study might provide a new perspective for the demand-side management program of net-zero emissions building.

Solving electric power distribution uncertainty using deep learning and incentive-based demand response

Recent Demand Response (DR) struggles with the end user's uncertainty in Electric Power Consumption (EPC), which affects the system's generation costs and stability. Incentive-based DR has offered to be an effective technique for mitigating supply and demand imbalances. However, it presents complex issues, such as electricity consumption uncertainty. This article proposes an incentive-based integrated DR model for Demand Side Management (DSM) program to handle the EPC uncertainty. In addition, the applicability of DR has been enhanced by the deep learning-based Bi-directional Long Short Term Memory (B-LSTM) model to forecast and curtail the load of the participated end users in the DSM program. Finally, results indicate that the proposed DSM program can achieve a win-win situation in reducing end-user uncertainty, lowering costs, and enhancing system stability.

Ameliorated class topper optimizer for cost optimization using demand side management program in a day-ahead energy market

Ameliorated class topper optimizer for cost optimization using demand side management program in a day-ahead energy market

Impact of demand side management approaches for the enhancement of voltage stability loadability and customer satisfaction index

This research work presents the tri-level optimization framework for the optimal scheduling of grid-connected and autonomous microgrids to diminish power losses and maximize loadability. Since the network's voltage profile depends on the loading level, the flexible load shaping-based demand-side management strategy is incorporated to investigate its impact on microgrid loadability. With the consideration of uncertain parameters related to renewable power generation, load demand, and power loss, voltage limit constraints, the resultant problem is formulated as a stochastic mixed-integer non-linear problem to enhance microgrid loadability and optimize daily operating costs. The interdependency of demand side management program and microgrid loadability is investigated. The seasonal load profiles covering the weekend and weekday loads in winter, summer, and spring/fall seasons are examined in this research work. The enhanced versions of the distribution networks IEEE-33 and IEEE-69 based microgrid test systems are chosen to evaluate the proposed framework in both off-grid and autonomous modes of operation. Simultaneously, the overall customer satisfaction index is evaluated and improved according to the seasonal load profiles winter weekday, winter-weekend, summer-weekday, summer-weekend, spring-weekday, and spring-weekend by 8.68%, 7.97%, 16.7%, 19.62%, 17.14%, 20.50% respectively. The recently reported Whale Optimization Algorithm is adopted to solve the proposed optimization problem, and the obtained simulation results are validated by comparing them with popular metaheuristic algorithms. The computational burden on the utility is reduced for optimal scheduling of grid-integrated microgrid to extract maximum power by maintaining network voltage profile.

Multi-layer energy management of smart integrated-energy microgrid systems considering generation and demand-side flexibility

This paper proposes a stochastic framework for the operation scheduling of integrated renewable-based energy microgrid systems. The proposed model presents comprehensive scheduling that simultaneously considers total generation costs, generation flexibility, and demand-side flexibility. This operation management approach is modeled as the tri-layer framework. At the first layer, the microgrid system attempts to minimize daily operation costs considering the probabilistic behavior of renewable generation, signal prices, and loads. The desalination unit and water tank storage have been incorporated into the proposed structure to supply potable water for the system. The second layer reschedules the obtained management of the first layer to increase the thermal flexibility and electrical flexibility of local generation resources. To this end, the integrated energy system tries to maximize the spinning reserve of the local energy resources in the second layer. The last layer is responsible to increase demand-side flexibility. In this layer, a hybrid max–min and min–max approach is developed to uniform the load profile by demand-side management programs. The proposed framework is applied to the general structure of energy systems and the day-ahead results demonstrate that the electrical generating flexibility index and thermal generating flexibility index are improved by 22.98% and 34.64% in the proposed model.

A new optimized demand management system for smart grid-based residential buildings adopting renewable and storage energies

Demand Side Management (DSM) implies intelligently managing load appliances in a Smart Grid (SG). DSM programs help customers save money by reducing their electricity bills, minimizing the utility’s peak demand, and improving load factor. To achieve these goals, this paper proposes a new load shifting-based optimal DSM model for scheduling residential users’ appliances. The proposed system effectively handles the challenges raised in the literature regarding the absence of using recent, easy, and more robust optimization techniques, a comparison procedure with well-established ones, using Renewable Energy Resources (RERs), Renewable Energy Storage (RES), and adopting consumer comfort. This system uses recent algorithms called Virulence Optimization Algorithm (VOA) and Earth Worm Optimization Algorithm (EWOA) for optimally shifting the time slots of shiftable appliances. The system adopts RERs, RES, as well as utility grid energy for supplying load appliances. This system takes into account user preferences, timing factors for each appliance, and a pricing signal for relocating shiftable appliances to flatten the energy demand profile. In order to figure out how much electricity users will have to pay, a Time Of Use (TOU) dynamic pricing scheme has been used. Using MATLAB simulation environment, we have made effectiveness-based comparisons of the adopted optimization algorithms with the well-established meta-heuristics and evolutionary algorithms (Genetic Algorithm (GA), Cuckoo Search Optimization (CSO), and Binary Particle Swarm Optimization (BPSO) in order to determine the most efficient one. Without adopting RES, the results indicate that VOA outperforms the other algorithms. The VOA enables 59% minimization in Peak-to-Average Ratio (PAR) of consumption energy and is more robust than other competitors. By incorporating RES, the EWOA, alongside the VOA, provides less deviation and a lower PAR. The VOA saves 76.19% of PAR, and the EWOA saves 73.8%, followed by the BPSO, GA, and CSO, respectively. The electricity consumption using VOA and EWOA-based DSM cost 217 and 210 USD cents, respectively, whereas non-scheduled consumption costs 273 USD cents and scheduling based on BPSO, GA, and CSO costs 219, 220, and 222 USD cents.

Energy Hub Planning and Operation Optimization by Applying Demand Side Management Using GAMS

Energy hub planning and optimization considering various constraints is an emerging domain of research due to the scarcity of conventional energy resources. Strategic integration of multi-energy sources is a dominant region of research as it provides better efficiency and cost savings. Various uncertainties due to the changing electric, heating, and cooling load demand are increasing the complexity of the system. The paper investigates the effectiveness of Demand Side Management (DSM) programs by analyzing the operation of domestic Energy Hub. The paper proposes two stages for optimal energy hub planning and operation optimization and GAMS mathematical modeling is used to deduce the results. The paper provides the analysis of a two-stage optimization model using the approach of modeling uncertainties and constraints. The process of energy hub designing has been carried out by the help of PSO algorithm by considering continuous capacities of various components of energy hub. The research regarding optimal energy hub operation is modelled in GAMS and the approach of mixed-integer non-linear programming (MINLP) has been used to evaluate the results. The framework provides the energy hub model and also provided the verification of model effectiveness and efficiency.

An energy demand-side management and net metering decision framework

Demand side management (DSM) and net metering (NM) are two important strategies that can be used in smart energy management systems to help utilities reduce peak loads and increase the penetration of renewable energy, while also enabling customers to save energy and reduce energy bills. In order to effectively implement DSM and NM programs, it is important to identify the customers who are most likely to benefit from these programs and to quantify the economic impact and benefit for both utilities and end-users. This paper proposes a decision framework that utilizes smart meter data and clustering analysis to identify representative load patterns, and a five-stage method for segmenting load patterns to create a consumption intensity matrix. A fuzzy ensemble based multi-criteria decision making framework is then applied to identify the most potential customer groups and quantify the economic benefit and impact. This paper presents a novel approach for identifying potential customers for DSM and NM programs and developing personalized services for energy demand management. The proposed framework is evaluated through a case study, which demonstrates its robustness in decision making and identifies 13 typical load patterns, seven for weekdays and six for weekends, representing diverse consumption behaviors. The weekday load patterns perform better than the weekend load patterns in terms of the five-stage method.