Demand Side Management Strategies Research Articles

An optimal demand side management for microgrid cost minimization considering renewables

AbstractIn an ordinary microgrid configuration, the required load changes from hour to hour. The power system firms determine the cost of energy at different times of day by considering the highest and lowest points of the consumption curve. This is referred to as time‐of‐use (TOU) pricing for power. The hourly basis load demand is divided into flexible and inflexible categories. Demand side management (DSM) lowers peak demand while rewarding customers for their participation based on load lowering. His rebuilds the whole load model on the pillars of demand cost movement. The research recommends a DSM methodology based on a combined intellect method to lower the total cost of employing loads in a microgrid (MG) structure while considering carbon tax as an unavoidable constraint to lower the emission of pollutants. This is because 40% of microgrid customers are willing to participate in the DSM scheme. The results obtained in each illustration demonstrate that the suggested DSM technique is suitable in terms of cost reduction. The generating cost was decreased from $15,488 to $15,354 when 0%–40% of clients engaged in the DSM programme. With just a 3% compromised increase in generation costs, a carbon price combined with economic emission dispatch reduced the pollutants emitted by up to 78%, from 70 to 15 tons.

A novel order characteristic load shifting policy for load factor improvement, peak reduction, and economical operation of distribution systems

Demand side management (DSM) and demand response programs (DRPs) are widely used economic strategies that insist customers shift or curtail loads to economize the generation cost of the distribution system. These policies often involve tedious calculation processes or optimization tools to calculate the apt amount of load to be shredded or shifted to get a new modified load profile. This paper proposes an order characterized load-shifting policy (OCLSP), which is very easy to calculate and yields a considerable decrease in the peak demand, eventually improving the distribution system's load factor, also known as the peak-to-average ratio (PAR). Unlike other DSM and DR strategies, the proposed approach requires no parameters except the forecasted load demand. The proposed OCLSP is implemented on ten distribution systems from the literature which consists of a 3-, 6-, 7-, 10-, 15- units system, one islanded microgrid (MG) system and four grid-connected MG systems. Initially, the load demand of these test systems is restructured using OCLSP, and thereafter, dynamic economic load dispatch (DELD) is performed to minimize the generation cost of the system. A latest circle search algorithm (CSA) is utilized as the optimization tool. Results show that the generation cost of all ten test systems has been decreased by a considerable amount when their load demand is restructured using the proposed OCLSP. The minimum percentage improvement in the PAR of the ten systems was 2 % and maximum improvement was 60 %. Likewise, the percentage decrement in the peak demand was within 2 % to 37.5 % for the ten test systems and the decrement in generation cost rose up to 16 %. All these parameters were in comparison with the base load demand. Numerical results also corroborate to the efficiency and robustness of proposed CSA.

Transition towards net zero emissions: Integration of a PV/T system with a hydroelectric generator and the impact of demand-side management

With the aim of diversifying different energy sources and achieving net zero emissions, hybrid renewable energy sources (HRES) represent the future of the world. However, several HRES simulation software do not integrate the Photovoltaic/thermal (PV/T) system. This article designs an optimal design model for a tri-hybrid Photovoltaic/thermal/hydroelectric (PV/T/H) system for a rural locality in the North Cameroon region. The two Demand Side Management (DSM) strategies used reveal that the DSM strategy significantly reduced the energy cost by 59 % and the emission by CO2 22 % compared to the No-DSM mode. Although the use of battery storage (BSS) is used in both cases, the optimal solutions obtained thanks to the multi-objective optimization method implemented on Matlab led to 418 PV/T panels, 2 MH generators, 2 diesel generators (DG) and 217 PV/T panels and 1 DG for DSM and No-DSM mode respectively. This study is a demonstration of the effect of dynamic tariffs and active demand management technologies on PV/T/H modeling and optimization. It also reveals the need to hybridize PV/T with other energy systems to increase performance and achieve net zero emissions.

A review of residential water conservation policies and attempts to measure their effectiveness

With escalating global water scarcity and increasing pressures on freshwater resources, demand-side management has emerged as a crucial tool for sustainable water resource management. This paper reviews residential, demand-side water management strategies, focusing primarily on price mechanisms. We trace the evolution of price structures and attempts to estimate consumer responses under these structures, highlighting the methodological and practical difficulties with estimating elasticity under non-linear billing structures. We also include a tertiary review of previous research into non-pecuniary strategies, such as restrictions, and information/education campaigns. This review serves as a primer for policymakers, water managers, and researchers seeking to design and evaluate demand-side management in residential water use.

Informing targeted Demand-Side Management: Leveraging appliance usage patterns to model residential energy demand heterogeneity

The transition towards decentralised and decarbonised electricity systems requires a detailed understanding of residential energy demand to inform effective Demand-Side Management (DSM) strategies. Current modelling approaches predominantly rely on socio-demographic factors to capture occupant behaviour heterogeneity, potentially overlooking critical household energy usage variability essential for low-voltage grid analysis.Alternatively, we propose a methodology centred on temporal and intensity patterns of appliance usage. Specifically, we develop an activity-based energy demand model that establishes pattern-dependent links between occupants' activity schedules and appliance switch-on events. Furthermore, we evaluate our approach's ability in estimating the distribution of DSM potential within the population, comparing it with conventional approaches based on socio-demographic classes.By examining laundry and dishwashing as case studies, the results demonstrate that our approach provides a more comprehensive characterisation of energy demand heterogeneity compared to conventional socio-demographic-based approaches, offering three key advantages: (i) enabling the identification and targeting of population segments with higher flexibility potential, (ii) determining the optimal contribution of different population segments to various flexibility services, and (iii) informing the design of actionable DSM interventions. Lastly, we propose strategies for operationalizing a pattern-oriented approach in real-world contexts and emphasise the need to integrate different data sources to advance DSM research.

Solar–Hydrogen-Storage Integrated Electric Vehicle Charging Stations with Demand-Side Management and Social Welfare Maximization

The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits.

Modeling a game to shift peak water demands: WaterTime leaderboard

Residential water demands vary with a diurnal pattern, and peak hour demands lead to inefficiencies in the operation and management of urban water distribution systems. Peak hourly demands generate significant costs due to the energy requirements of producing and pumping large volumes of potable water. High peak demands also require large investments in infrastructure expansion to support urban growth and economic development. This research develops an agent-based model to assess a demand-side management strategy that shifts high hourly demands. This research envisions a gamification system that rewards players through a leaderboard for shifting water end uses for laundry, irrigation, and bathing from on-peak to off-peak periods. Gamification of demand shifting behaviors is enabled through advanced metering infrastructure, which measures hourly or sub-hourly demands at the account level, and can be analyzed to assess water use behaviors. This research develops an Agent-based Modeling approach based on the Opinion Dynamics theory to calculate the changing opinion of household agents toward shifting water demands, based on utility broadcasts, observations of neighbor water use, social media, and participation in a leaderboard. The model is applied for eight months of hourly demand data collected through smart meters at a participating utility. Results show that a high level of participation in the leaderboard increases the adoption rate of customers shifting the time of specific end uses and may account for up to a 37 % reduction of the peak volume. The diurnal curve is flattened as consumers shift water use from on-peak to off-peak hours. The Agent-Based Model - Opinion Dynamics approach can assist water utilities in designing and evaluating gamification approaches that mitigate peak demands to support water infrastructure management.

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.

Practice of a Load Shifting Algorithm for Enhancing Community-Scale RES Utilization

Amidst the recent energy crisis, the pivotal roles of resource efficiency and renewable energy sources (RES) for sustainable development have become apparent. The transition to sustainability involves decentralized energy solutions empowering local communities to generate, store, and utilize their energy, diminishing the reliance on centralized systems and potentially transforming them into resources for power flexibility. Addressing the above necessitates, amongst other elements, the adoption of advanced demand-side management (DSM) strategies. In response, we introduce a versatile algorithm investigating the impact of DSM on the community scale, designed to maximize the utilization of renewable energy produced from local installations. Integrated as an ancillary module in a research data management platform, the algorithm underwent testing using historical datasets collected from end-consumers and a small-scale RES installation. This study not only offers insights for energy stakeholders, but also establishes theoretical parameters that can inform subsequent decision-making processes in the field.

Generating synthetic building electrical load profiles using machine learning based on the CRISP-ML(Q) framework

Abstract This study presents a machine learning application for generating synthetic building electrical load profiles. The implementation followed the Cross Industry Standard Process for the development of Machine Learning Applications with Quality assurance methodology, or CRISP-ML(Q) framework, to ensure a systematic machine learning development process. The model training performance was evaluated using the mean absolute error (MAE), root mean squared error (RSME), and coefficient of determination (R2) which were observed to be 0.0739, 0.1119, and 0.5728, respectively. These metrics remained consistent during the model testing phase, suggesting robust model performance. During the initial simulation experiment, the MAE and RMSE of the generated synthetic load profile were found to be 0.014 and 0.016, respectively, underscoring high model accuracy. Additional evaluation experiments showed that the developed machine learning application can generate realistic building load profiles using high-level parameters such as building type, average daily load, and peak demand. This study can aid in the development of demand-side management strategies and building energy management systems by providing realistic building electrical load profiles especially when real-world data is limited. For future work, researchers can consider integrating additional model features, refining data processing methods, and developing an agile version of the CRISP-ML(Q) framework.

Demand bidding vs. demand response for industrial electrical loads

We investigate a novel optimal demand bidding model for industrial loads based on an extended optimal power flow problem for the day-ahead market. The objective function accounts for generation costs and revenue resulting from the sale of products made by the bidder using electricity. As a bidding entity, the industrial load participates in electricity price formation, and is described using a model whose parameters can be determined mostly from public information. Constraints specific to industrial loads, such as satisfying product demand, are also included. The model is validated using simulations of a modified IEEE 24-bus reliability test case with a chlor-alkali plant as the load entity. We find that the proposed participation model, which is simple enough to include in grid dispatch software, performs well relative to other demand-side management strategies such as price-based demand response and cooperative scheduling of industrial load by the grid operator.

CaRDS - the statewide California Residential water Demand and Supply open dataset

As water scarcity becomes the new norm in the Western United States, states such as California have increased their efforts to improve water resilience. Achieving water security under climate change, population growth, and urbanization requires an integrated multi-sectoral approach, where adaptation strategies combine supply and demand management interventions. Yet, most studies consider supply-side and demand-side management strategies separately. Water conservation efforts are mainly driven by policy requirements and publicly available data to assess the effectiveness of demand- and supply-side management policies is often hard to find and unstructured. Here we present CaRDS - the statewide California Residential water Demand and Supply open dataset. CaRDS encompasses nine years (2013-2021) of monthly water supply and demand time series for 404 water suppliers in California, USA, compiled from different open-access data sources. Access to detailed temporal and spatial water supply operations and demands at the state-level can be useful to researchers and practitioners to realize applications such as evaluating the effectiveness of water conservation policies and discovering regional differences in water resilience measures.

Optimization of demand side management strategies for building clusters based on cooperative game theory

Optimization of demand side management strategies for building clusters based on cooperative game theory

Demand side management strategy for smart building using multi-objective hybrid optimization technique

This study proposes a home energy management system that uses the load-shifting technique for demand-side management as a way to improve the energy consumption patterns of a smart house. This system's goal is to optimize the energy of household appliances in order to effectively regulate load demand, with the end result being a reduction in the peak-to-average ratio (PAR) and a consequent minimization of electricity costs. This is accomplished while also keeping user comfort as a priority. Load scheduling based on both a next-day and real-time basis is what is used to meet the load demand requested by energy customers. In addition to providing a fitness criterion, utilizing a multi-objective hybrid optimization technique makes it easier to achieve an equitable distribution of workload between on-peak and off-peak hours. Moreover, the idea of developing coordination among home appliances in order to achieve real-time rescheduling is now being studied as a concept. Because of the inherent parallels between the two problems, the real-time rescheduling issue is framed as a knapsack problem and is solved using a dynamic programming strategy. The performance of the suggested methodology is evaluated in this study in relation to real-time pricing (RTP), time-of-use pricing (ToU), and crucial peak pricing (CPP). The simulation findings, which were assessed using a confidence interval that was set at 95 %, provide proof of the relevance that has been shown to be associated with the proposed optimization method. During scheduling RTP signal showcases a minimum PAR of 2.22 and a cost reduction of 24.06 % for HAG compared to the unscheduled case. Under the TOU tariff, HAG manages to reduce PAR by 46.14 % and cost by 20.44 %. Similarly, in the case of CPP, HAG outperforms by reducing PAR by up to 29.5 % and cost by up to 31.47 %.

Renewable-based microgrid energy management: A comprehensive exploration of techniques, strategies, and long-term viability

Renewable-based microgrid energy management systems have emerged as a promising solution to address energy security, sustainability and resilience concerns. This paper offers a comprehensive exploration of the techniques, strategies and long-term viability of such systems. The purpose of the study delves into the implementation of smart management and control systems, which enable real-time data analysis, grid optimization and effective load management, thus maximizing the utilization of renewable energy and enhancing overall system efficiency. It demonstrates the significance of decentralized energy management and its role in fostering energy independence and sustainability within local communities and small-scale industrial setups. The paper further investigates the resilience and redundancy aspects of microgrids, highlighting the importance of autonomous operation during grid failures and emergencies. Thus, the incorporation of energy-efficient practices, appliances and demand-side management strategies to promote energy conservation and reduce overall energy consumption of solar power, wind power, hydro power, biopower and geothermal. The important outreach and training are creating environmentally friendly energy consumption and improving the long-term sustainability of microgrid energy control systems that rely on energy generated from renewable sources. The study seeks to enhance ecological energy handling techniques and the utilization of clean sources of energy by offering knowledge regarding the promise of the microgrid to become a versatile and environmentally friendly power solution via this examination.

Time-series data clustering with load-shape preservation for identifying residential energy consumption behaviors

Categorizing residential energy demand patterns is a principal task for demand-side management (DSM) and energy-saving strategies. While deep learning (DL)-based clustering offers a promising alternative to conventional machine learning (ML), DL’s advantages and disadvantages over ML still remain unclear in identifying energy demand patterns. Moreover, prevalent DL-based clustering can suffer from catastrophic feature distortion when capturing load-shape information from energy-load data, leading to erroneous pattern identification. To address these issues, we propose integrating a load-shape preservation mechanism into representative DL-based clustering and investigate its effectiveness in categorizing energy demand patterns, compared to existing ML and DL. We experiment and compare the three clustering approaches using one-year residential energy-load data. Results show that the proposed DL, equipped with load-shape preservation, outperformed ML quantitatively and closely aligns with the baseline DL’s performance. This is particularly significant considering that the baseline DL prioritizes quantitative enhancements, sometimes compromising load-shape precision. Furthermore, the proposed DL discovered more diverse energy demand patterns than the baseline ML and DL, while producing more human-agreeable results. This finding underscores the pivotal role of load-shape preservation in enhancing data clustering and demand pattern recognition in the real-world. These benefits will facilitate personalized DSM interventions and foster residents’ energy-saving behaviors.

Optimal management of microgrid energy by considering demand side management plan and maintenance cost with developed particle swarm algorithm

This paper introduces a pioneering approach to power system optimization by integrating microgrids and demand-side management, with a focus on its novel contributions. Emphasizing cost reduction, the study targets effective demand management through load shifting. A unique microgrid energy management system is proposed for peak-shaving, leveraging the cost advantages of lower controllable unit expenses. The system actively involves microgrid units in responsive load management, reducing peak consumption during high-demand periods. Evaluation of various demand-side management techniques, especially peak-time rebate programs, is conducted through presented indices measuring the energy management solution's effectiveness. To address the complexity, a particle swarm-based algorithm is introduced for computational efficiency. Validation of the proposed method and model in diverse scenarios showcases significant cost reduction, demonstrating the practicality and innovation of the integrated microgrid and demand-side management strategy. The study highlights a substantial reduction in peak load through load response program participation. At a 20 % participation rate with a 10-cent incentive, there's a significant decrease in peak load during specific hours. With increasing participation and incentives, peak load reduction intensifies, reaching optimal results at a 40 % participation rate and a 20-cent incentive.

A comparative study of demand-side energy management strategies for building integrated photovoltaics-battery and electric vehicles (EVs) in diversified building communities

This study compares four developed energy management strategies for a grid-connected photovoltaic-battery (PVB) system in a district energy system comprising four diverse building communities: campus, residential, office, and commercial. The proposed demand-side energy management scenarios include maximizing photovoltaic self-consumption, cost minimum strategy under time-of-use and two peer-to-peer (P2P) strategies (a Mid-Market Rate (MMR) and a novel demand response (DR) based P2P strategies). Besides, Monte Carlo integrated Markov Chain simulation method was applied to calculate the load distribution of integrated electric vehicles in diversified building communities, and P2P price was generated considering dynamic supply-demand ratio. In term of dynamic energy flow, energy cost and grid interaction, dispatch results of proposed management strategies were simulated and compared in detailed. The results demonstrate that compared to the mainstream MSC and TOU operation strategies, P2P strategies, especially the DR based P2P strategy, effectively alleviate pressure on the utility grid by actively responding to peak electricity demand and utilizing excess renewable energy generation from other communities. It is worth noting that the choice of strategy depends on the role of the building community, whether as a consumer or a prosumer. This study offers insights for decision-makers and stakeholders in managing grid-connected PVB district energy systems in diverse building communities.

Can you lower the thermostat? Perceptions of demand response programs in a sample from Quebec

The current push towards electrification in various sectors as a decarbonization strategy presents significant challenges for utility providers due to unintended consequences related to higher peak demand. To ensure grid reliability, utilities resort to importing costly electricity or using fossil fuels, which counteracts these decarbonization efforts. To address this issue, they introduced demand-side management strategies, known as demand-response (DR) programs, to engage customers in reducing peak demand. While commercial and industrial customers have greater load flexibility, the significance of residential buildings in managing peak demand is growing due to electrification of heating systems and the proliferation of electric vehicles (EV). However, the development of residential DR programs lags behind their potential, facing challenges such as limited customer knowledge, price elasticity, and infrastructure costs. To increase residential DR program adoption, understanding occupants' perceptions and behaviors is vital. This paper presents the results of a survey conducted in Quebec to identify participation rates, incentives, approaches, and contextual factors influencing enrolment in DR programs and participation in peak demand events. It was found that there is significant potential for expanding these programs, especially by identifying key demographic sectors that may be more likely to participate compared to the current participant breakdown. The research also uncovered contextual factors and demographic variables that statistically influenced occupant behavior during peak demand events and their impact on energy-saving actions. Notably, more than 80 % of respondents believed that having smart thermostats would facilitate their participation in DR programs. These results offer valuable insights for further development and enhancement of DR programs in the province.

Assessing the Flexibility Potential of Industrial Heat–Electricity Sector Coupling through High-Temperature Heat Pumps: The Case Study of Belgium

Thermal processes represent a significant fraction of industrial energy consumptions, and they rely mainly on fossil fuels. Thanks to technological innovation, highly efficient devices such as high-temperature heat pumps are becoming a promising solution for the electrification of industrial heat. These technologies allow for recovering waste heat sources and upgrading them at temperatures up to 200 °C. Moreover, the coupling of these devices with thermal storage units can unlock the flexibility potential deriving from the industrial sector electrification by means of Demand-Side Management strategies. The aim of this paper is to quantify the impact on the energy system due to the integration of industrial high-temperature heat pumps and thermal storage units by means of a detailed demand–supply model. To do that, the industrial heat demand is investigated through a set of thermal process archetypes. High-temperature heat pumps and thermal storage units for industrial use are included in the open-source unit commitment and optimal dispatch model Dispa-SET used for the representation of the energy system. The case study analyzed is Belgium, and the analysis is performed for different renewable penetration scenarios in 2040 and 2050. The results demonstrate the importance of a proper sizing of the heat pump and thermal storage capacity. Furthermore, it is obtained that the electrification of the thermal demand of industrial processes improves the environmental impact (84% reduction in CO2 emissions), but the positive effect of the energy flexibility provided by the heat pumps is appreciated only in the presence of a very high penetration of renewable energy sources.