Demand Side Management Approach Research Articles

A novel probabilistic load shifting approach for demand side management of residential users

Demand side management is a beneficial set of techniques which leads improving consumption profile of residential users. Practice of dynamic pricing and appropriate shifting of electrical appliances at home are important tasks at that point. In this study, a novel probabilistic load shifting strategy is proposed and its beneficial effects on economic and technical parameters are demonstrated. For that purpose, a survey on energy consumption is applied for residential users and its results are used for creating a probabilistic model. 300 virtual residents are created in this way and a probabilistic simulation technique is developed. Next, the developed simulation technique is applied for creation of the monthly consumption data. Finally, it is demonstrated that electricity bills are decreased significantly when the proposed approach is applied. Besides that, peak-to-average ratio which is calculated by considering all residential users is also decreased as a result. In conclusion, when the results are compared with the ones of conventional constant tariff, time-of-use tariff with three time zones and a deterministic load shifting strategy, it is observed that the best results of economic and technical parameters in question are achieved by using the proposed probabilistic load shifting strategy together with dynamic pricing mechanism involved.

Demand side management through energy efficiency measures for the sustainable energy future of Pakistan

Pakistan is facing energy crises due to localized shortages, market manipulation, infrastructure disruption, rising demand, governance issues, climate and geopolitical events. In this situation Demand Side Management (DSM) is a promising solution to overcome the problem of energy crises. DSM strategy helps to manage consumer demand through energy conservation rather than to addition of new power capacity. In this study, Low Emissions Analysis Platform (LEAP) develops an energy model for Pakistan for the period 2022–2050. Three scenarios has been to constructed namely Baseline (BAS), Green Energy Policy (GEP), and Energy Efficiency (ENE) to predict the future energy demand, production, carbon emissions and the investment cost which covers capital, operational and maintenance costs. The model results suggest that DSM targets should be achieved through the implementation of ENE scenario. Predicted energy production and consumption under the ENE scenario are substantially less than those under the BAS scenario. The country can meet its 635.83,000 GWh energy demand with its 747.15,000 GWh energy production. Non-renewable sources produce 171.27,000 GWh, whilst renewable sources produce 575.88,000 GWh. According to this scenario, by 2050, CO2 emissions will be produced around 93.16 million metric tons, requiring an investment cost of $46.80 billion for building new power capacity. The study provides a roadmap with a suggestive optimal balance between energy saving with DSM approach and utilizing renewable energy production to meet energy demand for different sectors of the economy.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Optimal energy storage system design for addressing uncertainty issues in integration of supply and demand-side management approaches

The primary goal of Sustainable Development Goal 7 (SDG 7) is to increase renewable energy use to reduce reliance on fossil fuels and mitigate climate change. Energy-intensive industries can benefit from in-house renewable power generation, reducing their reliance on fossil fuel-based grid power and making processes greener. However, integration among power generation/purchase, energy storage systems (ESS), and power consumption is crucial to overcome the intermittent nature of renewable power sources. ESS plays a vital role in increasing resilience and optimizing material production based on power availability and pricing. The efficacy of ESS needs critical evaluation considering cost, efficiency, and uncertainties related to renewable power generation and material product demand. The paper proposes two scenario-based optimization approaches to assess the impact of uncertainties on the integrated supply and demand side management (ISDM) system, focusing on lithium-ion batteries and cryogenic energy storage (CES). Compared to the conservative approach of the scenario-based robust optimization (SRO) method, the proposed stochastic simulation optimization (SSO) method provides a ‘risk-neutral’ solution, which is 6.45% less than the minimum expected cost solution. The analysis also suggests that lithium-ion batteries are more economically effective for the proposed integrated framework than CES, resulting in almost a 29% reduction in operating costs compared to no battery option. The proposed framework could contribute to sustainable and economically viable energy management practices in energy-intensive industries. Further research and implementation of such frameworks could accelerate the adoption of renewable energy and energy storage technologies in industrial processes.

A swarm-intelligent based load-shifting strategy for clean and economic microgrid operation

The required load in a typical microgrid structure fluctuates from hour to hour. Based on the peaks and troughs of the load demand curve, the power system utilities preset different price of electricity at various periods of the same day which is referred to as time-of-use (TOU) pricing policy. An optimal economic strategy called demand side management (DSM) reduces load during peak hours by shifting the elastic loads and compensates the shifted load by increasing the demand during valley hours. This recreates the total demand pattern on the demand price elasticity relation. The novel benefaction of this research suggests a DSM approach based on a hybrid intelligence technique to attain a compromised solution between minimum generation costs and pollutants emitted by DERs of an LV grid-connected microgrid system. Several grid involvement strategies, power market pricing types, and demand response mechanisms are examined in five separate instances for the microgrid system. The outcomes achieved in each example show that the recommended DSM technique may be applied and is appropriate in terms of cost reduction. When 30–40% of customers participated in the DSM curriculum, the generation cost was reduced by 10–13%. Environment constrained economic dispatch provided a superior compromise amongst lowest generation cost and emission for different microgrid load models. Statistical analysis and comparison of the results from previously published literatures validates the superiority of the proposed approach incorporated with DSM policy.

Cost-Centric Energy Management of a Test Grid System Considering Load Flexibility for DSM

Demand-Side Management (DSM) adjusts load demand by rebuilding the load demand model of a distribution system and thus reduces operating costs. One potential approach to achieving cost savings is the redistribution of flexible loads to time periods characterized by lower utility expenses per unit, or alternatively, the elimination of nonessential loads. The primary objective of this research is to examine the use of DSM approaches in the scheduling of domestic appliances. The aim is to effectively minimize operating expenditures, the peak-to-average demand ratio (PADR), and any associated problems. Domestic appliances may be categorized into three distinct types based on their ability to adjust to variations in time, temperature, and light: time-flexible, temperature-flexible, and light-flexible. The second step of the study focuses on modelling the power consumption scheduling issue for demand-side clients. This is done by including operating cost priorities and modes of operation while taking into account both demand and supply factors. This research optimizes an IEEE 33 bus test grid system using load scheduling, fossil fuel generators, and renewable energy. The primary objective of this optimization is to minimize operating costs. In order to optimize the load model, it was necessary to restructure it according to the DSM participation level. After considering the restructured load demand models, distributed load scheduling ideas are disseminated to reduce test grid system power distribution costs.

Two-Stage Approach for Demand Side Management in Multi-Smart Grids

In the face of escalating electricity demand driven by technological advancements and a growing population, efficient management becomes imperative. This study explores demand-side management (DSM) within smart grids (SGs) to minimize customer bills, alleviate peak loads, and mitigate losses. The research employs diverse strategies and compares their effectiveness, focusing on optimal load consumption across smart microgrids. Key contributions include a DSM strategy for cost reduction, efficient peak load reduction, and active power loss minimization in smart microgrids. The proposed approach is validated through simulations, with results contributing to real-world SG implementation. The study highlights a 23.4% and 31.6% cost saving for residential and commercial customers, respectively, and emphasizes the significance of load management in enhancing operational efficiency and cost-effectiveness within the energy hub framework. The research provides valuable insights for developing advanced consumption management strategies, ensuring energy efficiency and sustainability in future power systems.

Evaluation of stand-alone hybrid renewable energy system with excess electricity minimizer predictive dispatch strategy

The prioritization of economic and reliability objectives in the optimization of stand-alone microgrids and off-grid hybrid renewable energy systems (HRES) has led to optimum systems with high excess electricity and low self-consumption indexes. Managing surplus power after charging the storage bank without imposing a negative impact on energy cost (COE) and environmental factors is a challenge for off-grid applications. In recent years the development of predictive dispatch strategies (PD), improves the energy efficiency in HRES compared to conventional dispatches such as load following (LF). However, the mismatch between renewable power production and demand profiles prohibits high excess electricity reduction. In this study, a novel dispatch strategy, entitled modified predictive dispatch (MPD), is developed by integrating a proactive approach to charging/discharging the battery bank and demand-side management (DSM) based on the upcoming short-term excess electricity profile. The results showed that with the participation of an average of less than 20 % deferrable components and the possibility of load shifting for less than 3 h, the excess electricity can be reduced to about 7.5 %, compared to 41 % and 18.5 % for LF and PD strategies, respectively. Furthermore, the self-utilization index of surplus power improved from less than 25 % to about 90 %. These improvements were achieved through the MPD strategy, which reduced COE by more than 10 %, reaching about 0.12 $/kWh without any significant effect on emission factors. The sensitivity analysis indicated that achieving an excess electricity self-utilization index of over 80 % requires a load-shifting possibility of more than 2 h and a deferrable load participation of about 10 % in the DSM approach. Therefore, results approved the effectiveness of the proposed dispatch strategy in high renewable penetration systems, even with low levels of participation by power consumers.

Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning

Greenhouses are key to ensuring food security and realizing a sustainable future for agriculture. However, to ensure crop growth efficiency, greenhouses consume a significant amount of energy, primarily through climate control and artificial lighting systems. Owing to this high energy consumption, a network of greenhouses exhibits immense potential to participate in demand response programs for power grid stability. In this work, a multi-agent deep reinforcement learning (MADRL) control framework utilizing an actor-critic algorithm with a shared attention mechanism is proposed for energy management in networked greenhouses. A network of renewable energy integrated greenhouses is constructed to interact with the power grid, when necessary, to address the fluctuations associated with renewable energy generation and dynamic electricity prices. The viability and scalability of this multi-agent approach is demonstrated by evaluating its capabilities for a network of five greenhouses of varying capacities. The proposed MADRL-based control approach for demand-side energy management in networked greenhouses demonstrates efficiency in maintaining indoor climate in all greenhouses while ensuring a 28% reduction in net load demand as compared to well-known algorithms.

A sustainable approach for demand side management considering demand response and renewable energy in smart grids

The development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. To address this issue, energy management controllers (EMCs) have emerged as automated solutions for energy management problems using DR signals. This study introduces a novel hybrid algorithm called the hybrid genetic bacteria foraging optimization algorithm (HGBFOA), which combines the desirable features of the genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in its design and implementation. The proposed HGBFOA-based EMC effectively solves energy management problems for four categories of residential loads: time elastic, power elastic, critical, and hybrid. By leveraging the characteristics of GA and BFOA, the HGBFOA algorithm achieves an efficient appliance scheduling mechanism, reduced energy consumption, minimized peak-to-average ratio (PAR), cost optimization, and improved user comfort level. To evaluate the performance of HGBFOA, comparisons were made with other well-known algorithms, including the particle swarm optimization algorithm (PSO), GA, BFOA, and hybrid genetic particle optimization algorithm (HGPO). The results demonstrate that the HGBFOA algorithm outperforms existing algorithms in terms of scheduling, energy consumption, power costs, PAR, and user comfort.

Integration of renewable energy resources into the water-energy-food nexus–Modeling a demand side management approach and application to a microgrid farm in Morocco

This paper introduces a framework aimed at analyzing the water-energy-food nexus (WEF) within the context of sustainable farming practices utilizing renewable energy sources, specifically Solar PV, to optimize water management efficiency. The focus lies on investigating the interplay between the water-energy-food nexus and both technical and institutional factors. The study particularly delves into the utilization of distributed energy systems and microgrids for electricity distribution. To achieve the objectives outlined, the framework is applied to a case study involving an off-grid farm in Morocco, aligned with the country's “Green Morocco Plan” of 2008. The study employs the AnyMOD open-source modeling framework in combination with the publicly available decision support tool CropWat (Version 8.0). Through this coupling, a linear optimization model is created to assess various irrigation practices, thus evaluating the energy and water supply variations across different crop growth stages. By employing scenario analysis, the study reveals that the integration of a smart microgrid alongside storage technologies proves beneficial in terms of reducing overall system costs. This integration presents cost-effective solutions and enables the establishment of a sustainable energy supply driven by renewable energy resources. Furthermore, the investigation highlights that constraining irrigation to specific hours of the day results in increased storage requirements and higher associated costs. In conclusion, the study underscores that enhancing the water-energy-food nexus through the integration of a renewable-based microgrid is a complex task. However, it significantly contributes to the development of sustainable farming solutions. This research sheds light on the challenges and opportunities associated with aligning renewable energy, water management, and agricultural practices, ultimately facilitating the pursuit of environmentally conscious and efficient farming methodologies.

Real-time demand response strategy base on price and incentive considering multi-energy in smart grid: A bi-level optimization method

Real-time pricing (RTP) is an efficient approach for demand side management, which mainly aims at reducing peak-to-average ratio (PAR) and balancing power supply and demand. However, in real life, users’ real power consumption sometimes deviates significantly from the ordering power, which results in waste of power resources. Only the price-based demand response (FBDR) is difficult to tackle the deviation problem, especially in the multi-energy generation system. To address this issue, a two-stage hybrid demand response (DR) strategy, which combines RTP and real-time incentive, has been proposed in this paper to minimize the difference between the real power consumption and the ordering power. The first stage is to determine the optimal ordering power via RTP strategy, and then the power supplier and users reach an agreement of power ordering (APO). The second stage is to minimize the difference between the real power consumption and the ordering power via real-time incentive and APO. According to the random dynamic interaction between users and the power supplier, a bi-level model is constructed. Multi-energy generation and energy storage system (ESS) on the supply side, and photovoltaic (PV) generation on the demand side are also considered in this model. Considering the characteristics of the upper and lower objectives and the benefits of both supply and demand sides, the corresponding distributed optimization algorithm is designed to solve the model. Finally, the real-time price, optimal power consumption and optimal power supply are obtained. The simulation results show that the proposed DR strategy and algorithm not only fully guarantee the benefits of users and the power supplier, but also have a good performance in shaving peak and filling valley.

Long-term experimental study of price responsive predictive control in a real occupied single-family house with heat pump

The continuous introduction of renewable electricity and increased consumption through electrification of the transport and heating sector challenges grid stability. This study investigates load shifting through demand side management as a solution. We present a four-month experimental study of a low-complexity, hierarchical Model Predictive Control approach for demand side management in a near-zero emission occupied single-family house in Denmark. The control algorithm uses a price signal, weather forecast, a single-zone building model, and a non-linear heat pump efficiency model to generate a space-heating schedule. The weather-compensated, commercial heat pump is made to act smart grid-ready through outdoor temperature input override to enable heat boosting and forced stops to accommodate the heating schedule. The cost reduction from the controller ranged from 2-17% depending on the chosen comfort level. The experiment demonstrates that load shifting is feasible and cost-effective, even without energy storage, and that the current price scheme provides an incentive for Danish end-consumers to shift heating loads. However, issues related to controlling the heat pump through input-manipulation were identified, and the authors propose a more promising path forward involving coordination with manufacturers and regulators to make commercial heat pumps truly smart grid-ready.

Integrated demand-side management for multi-energy system based on non-cooperative game and multi-energy pricing

This paper proposes an integrated demand-side management (IDSM) approach based on non-cooperative game and multi-energy pricing strategy for a regional integrated energy system (RIES). On the demand side, the competition among multi-energy users was modeled as a non-cooperative energy consumption game, and the conditions to ensure the existence and uniqueness of the Nash equilibrium of the non-cooperative game were given. Considering the privacy of user information, a distributed multi energy consumption control algorithm based on dual theory and gradient descent method was designed to search for the optimal multi-energy consumption and the multi-energy daily retail price. In addition, for the energy supply side, the scheduling strategy of energy hub (EH) was proposed based on linear programming (LP) theory to optimize equipment operation. The results show that this approach can enable users and EH to coordinate optimization, so as to reduce the peak-to-average ratio (PAR) and energy cost of the system, and ensure the balance between supply and demand of multi-energy.

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.

A Survey on Information Communication Technologies in Modern Demand-Side Management for Smart Grids: Challenges, Solutions, and Opportunities

The energy transition-revolution paradigm is coming with a new vision of interaction models to smartly manage the energy and data exchanged between all participants in the whole power system with solid regard to sustainability, resilience, cybersecurity and privacy. Added to the switching from fossil to renewable power sources the Demand-Side Management (DSM) which is a mix of software and hardware models with data analytics capabilities, is a crucial enabler of the energy transaction. More than the demand-Response (DR) functionalities, the DSM includes the overall management of the grid infrastructure while minimizing consumers’ discomfort and maximizing the grid stability with respect to environmental commitments. This paper reviews the application of several methodologies for DSM within the context of modern power grids. Existing DSM approaches are described and categorized while offering informative discussions to help reason through all considered studies. Future research avenues are pointed by suggesting DSM improvements for the context of the global trends in data analysis/cybersecurity for liberalizing electricity markets based on energy transactions and DSM-based communities, especially for Smart Cities. The standardization, the regulations, the data privacy, the cybersecurity issues, and their contributions to the energy system were reviewed as open issues in the context of a modern DSM.

Experimental validation of a state-of-the-art model predictive control approach for demand side management with a hot water heat pump

Hot water heat pumps are well suited for demand side management, as the heat pump market faced a rapid growth in the past years with the trend to decentralized domestic hot water use. Sales were accelerated through wants and needs of energy conservation, energy efficiency, and less restrictive rules regarding Legionella. While in literature the model predictive control potential for heat pumps is commonly shown in simulations, the share of experimental studies is relatively low. To this day, experimental studies considering solely domestic hot water use are not available. In this paper, the realistic achievable model predictive control potential of a hot water heat pump is compared to the standard hysteresis control, to provide an experimental proof. We show for the first time, how state-of-the-art approaches (model predictive control, system identification, live state estimation, and demand prediction) can be transferred from electric hot water heaters to hot water heat pumps, combined, and implemented into a real-world hot water heat pump setup. The optimization approach, embedded in a realistic experimental setting, leads to a decrease in electric energy demand and cost per unit electricity by approximately 12% and 14%, respectively. Further, an increase in efficiency by approximately 13% has been achieved.

A demand side management approach to increase self-consumption in buildings

There is a growing interest in increasing the presence of renewable energy in the electric network. Photovoltaic production from grid-connected systems is leading this growth in terms of households. Alongside this development, concern about network security has emerged, because excesses of intermittent renewable energy on the grid could exceed voltage limits. Self-consumption, understood as the capacity of the producer to consume his or her own production, can partially solve these problems. Thermostatic controllable loads, such as heating and cooling, represent 50% of the total amount of energy consumed by buildings; the proper allocation of these loads could be a driving force for self-consumption. In this study, a demand side management strategy is proposed based on a building energy model equipped with an inverter heat pump coupled with a photovoltaic plant. The goal is to maximize the use of local energy from the photovoltaic plant (self-consumption), reducing the export and import of energy to and from the grid. This goal is achieved by optimizing the set-points in each room. An array of optimal set-points over six years is presented. The results show the capacity of the methodology to match similar values of self-consumption (70% in winter and 50% in summer) obtained by strategies based on chemical batteries. The findings are shown in an energy matching chart at different levels of detail (yearly and monthly). Color bubbles are added to the matching chart to help visualize the unmatched energy of the system graphically. In comparison with actual model predictive control technologies, this study’s strategy offers great simplicity and a large saving in computational time.

Distributed Demand Side Management With Stochastic Wind Power Forecasting

In this article, we propose a distributed demand-side management (DSM) approach for smart grids taking into account uncertainty in wind power forecasting. The smart grid model comprehends traditional users as well as active users (prosumers). Through a rolling-horizon approach, prosumers participate in a DSM program, aiming at minimizing their cost in the presence of uncertain wind power generation by a game theory approach. We assume that each user selfishly formulates its grid optimization problem as a noncooperative game. The core challenge in this article is defining an approach to cope with the uncertainty in wind power availability. We tackle this issue from two different sides: by employing the expected value to define a deterministic counterpart for the problem and by adopting a stochastic approximated framework. In the latter case, we employ the sample average approximation (SAA) technique, whose results are based on a probability density function (PDF) for the wind speed forecasts. We improve the PDF by using historical wind speed data, and by employing a control index that takes into account the weather condition stability. Numerical simulations on a real data set show that the proposed stochastic strategy generates lower individual costs compared to the standard expected value approach.

Artificial Intelligence Based Demand Side Management Approach to Solve Non-Linear Dynamic Economic Emission Dispatch Problem

Artificial Intelligence Based Demand Side Management Approach to Solve Non-Linear Dynamic Economic Emission Dispatch Problem