Demand Side Management Strategies Research Articles

Optimizing power grids: A valley-filling heuristic for energy-efficient electric vehicle charging.

The expansion of electric vehicles (EVs) challenges electricity grids by increasing charging demand, thereby making Demand-Side Management (DSM) strategies essential to maintaining balance between supply and demand. Among these strategies, the Valley-Filling approach has emerged as a promising method to optimize renewable energy utilization and alleviate grid stress. This study introduces a novel heuristic, Load Conservation Valley-Filling (LCVF), which builds on the Classical and Optimistic Valley-Filling approaches by incorporating dynamic load conservation principles, enabling better alignment of EV charging with grid capacity. We conducted a comprehensive analysis of the heuristic across five EV charging scenarios. In both the Original and Flexible scenarios, LCVF reduced energy demand by up to 10.65%, demonstrating its adaptability and effectiveness. Notably, in the 24-hour Availability scenario, LCVF achieved a reduction of over 20% in energy demand compared to CVF. These findings indicate that LCVF could play a crucial role in enhancing real-world EV charging infrastructure, boosting energy efficiency and grid stability. By integrating DSM strategies like LCVF, energy grids can better accommodate renewable energy sources, promoting more sustainable operations.

Optimal energy management in a grid-tied solar PV-battery microgrid for a public building under demand response

Commercial buildings consume a substantial amount of energy, underscoring the need for consumption optimization. Embedded microgrids combined with demand side management strategies have potential to help end-users and utilities to better manage the grid. This paper presents an integrated optimal control stratrgy for a grid-tied solar PV-battery microgrid powering a public building under demand response program. Current literature predominantly focuses on residential buildings and, most importantly, only addresses a limited range of energy management objectives. In this study, a mixed-integer non-linear programming (MINLP) mathematical model that minimizes grid energy costs, grid peak demand, end-user inconvenience associated of appliance scheduling, and battery degradation is presented. To test and validate the model, two case study scenarios are considered: Case I applies the proposed model to a grid-tied, solar-PV-battery powered public building without demand response, while Case II integrates demand response into the resulting hybrid energy system. Simulation results from Case I demonstrate the model’s potential to reduce the building’s energy costs by 49%, with an additional 4.4% reduction achieved in Case II. Compared to the baseline model, the Case II model achieves a 37.5% reduction in system peak demand (coincident peak) and a 21.8% decrease in the building’s non-coincident peak demand. The proposed microgrid retrofit model is economically viable, with a payback period of 9-10 years. The obtained results can be used as a decision-making framework for planning embedded microgrids in public buildings with demand response programs.

Analysis of Qatar's electricity landscape: Insights from load profiling, clustering, and policy recommendations

Demand side management (DSM) is an important strategy for promoting sustainable consumption in resource-rich countries with high purchasing power and subsidized tariffs. Global energy and environmental resource consumption have increased rapidly due to advances in production and transportation, leading to inefficient and wasteful use of resources. DSM aims to address these issues by promoting efficiency. To implement appropriate DSM strategies, a greater understanding of consumer behavior is needed. To do so, load profiling and load clustering are two popular methods that can be used. This paper aims to i) summarize the most recent global load profiling and clustering works ii) use official smart meter data to understand key electricity consumption trends in Qatar, such as temperature-demand correlation, weekend vs. weekday, and public holiday consumption patterns, and iii) perform load clustering to propose policies that would help manage the electricity load in Qatar for its green growth. This study provides insights into the electricity consumption trends of various sectors in Qatar, including commercial, government, hospitality, and residential sectors. It was found that among all the sectors there were only two usage periods of the same time. There is naturally a strong correlation between temperature and electricity consumption throughout the sectors. Furthermore, it was observed that the consumption of the sectors is highly similar which leads to multiple sectors being present in the same cluster. Finally, policy changes are proposed based on the results to encourage demand response programs in Qatar.

Enhancing Demand-Side Flexibility to Reduce Grid Stress and Maximize Off-Peak Pricing Benefits

As energy demand and energy security increase rapidly, energy management has become a global issue and concern. The main objective of this study was to optimize hospital energy consumption by scheduling hospital consumption based on time-of-use (TOU) pricing. Removing load from the power system, changing load demand from peak hours to off-peak hours, and switching off-appliance are generally demand-side management strategies employed to lower energy costs at the expense of consumers’ comfort. To address the problems of appliances turned “off” during peak periods which are unacceptable by consumers, PV/battery systems are incorporated into the grid system to improve power balance and total energy costs. Most recent studies focused on optimizing energy consumption between the cost and value of micro-grid operation during a certain time period. This study proposed a fuzzy logic controller integrated energy management for commercial loads. The proposed method intelligently selects energy sources using the grid energy cost, remaining daily limit, load consumption and availability of the solar PV or battery at any time of the day. The control system operates the loads at a reduced cost without any shifting. MATLAB/Simulink was used to model the system. The controller can optimize and improve power consumption, power system stability and electricity cost. The simulation results showcase the capability of the proposed model to manage and control systems in a more intelligent manner compared to traditional control systems. Furthermore, after the introduction of the fuzzy logic controller system, a reduction of 20.8% in total energy consumption and 18.64% in electricity cost was achieved.

A hybrid machine learning-based cyber-threat mitigation in energy and flexibility scheduling of interconnected local energy networks considering a negawatt demand response portfolio

The interconnection of local energy networks (LENs) enables efficient exchange of energy and flexibility among them, fostering the integration of distributed energy resources and demand-side management strategies. Thus, the interconnected local energy systems (ILEN) structure is a viable approach to electrical distribution systems’ operation and management. However, implementing distributed energy management structures such as ILEN entails a great amount of information transactions. Therefore, these structures are more vulnerable to cyber threats. Thus, the newly developed efficient and secure power systems’ operation methods should take digitalization-related security risks into account. As a result, this paper is focused on the development of a secure operation method, equipped with a hybrid algorithm to mitigate cyber threats in the context of ILEN. In this regard, this research proposes a novel hybrid XGBoost-based cyber threat mitigation (HXGBTM) method to cope with the vulnerabilities of the physical and information layers of the cyber-infrastructure. The proposed cyber threat mitigation method is built upon the classification and regression capabilities of the XGBoost ensemble of decision trees to identify and mitigate anomalies in the electrical consumption data. Therefore, in the first step, the ILEN’s multi-objective energy and flexibility scheduling problem considering demand response portfolio i.e., MOEFSDRPILENis developed that encompasses a bi-level optimization problem, in which the operator of the ILEN optimizes energy and flexibility trading in the upper level. While in the lower level, each LEN operator minimizes scheduling costs along with maximizing the local flexibility as well as providing a demand response portfolio as a negawatt resource. Here, the flexibility index, which is later maximized using the second objective function, is considered as the proportion between "the available ramping capacity" and "required ramping capacity". In this paper, direct load control, and interruptible/curtailable demand response comprehensive models are implemented as candidate programs for the suggested portfolio. Furthermore, a hybrid cyber threat is modeled considering the communication line intrinsic vulnerability, as a result of natural causes, wear, and aging of the infrastructure etc., as well as false data injection (FDI) attacks that target each LEN’s electrical consumption database. Finally, the proposed HXGBTM is employed to mitigate the above-mentioned cyber-vulnerabilities and achieve near real-world conditions.

Synergistic Effects of Energy Storage Systems and Demand-Side Management in Optimizing Zero-Carbon Smart Grid Systems

The urgent need to mitigate climate change and reduce reliance on fossil fuels has driven the global shift towards renewable energy sources (RESs). However, the intermittent nature of RESs poses significant challenges to the widespread adoption of Zero-Carbon Smart Grids (ZCSGs). This study proposes a synergistic framework to address this hurdle. It utilizes energy storage systems (ESSs) by comparing Vanadium redox flow batteries (VRFBs) and Lithium ion batteries (LIBs) to identify the most suitable option for ZCSGs, with precise models enabling robust performance evaluation. Moreover, an accurate demand-side management (DSM) strategy considering power elasticity to manage discrepancies between electricity load, RES generation, and ESS availability is introduced for estimating fair, dynamic tariffs. An advanced load and weather-forecasting strategy is introduced for improving grid planning and management. An advanced optimization algorithm enhances grid stability and efficiency. Simulations demonstrate significant reductions in carbon footprint, peak power demand, and reliance on fossil fuels. The study finds that VRFBs outperform LIBs in cost and security, and dynamic tariffs based on accurate DSM significantly reduce energy costs. This work explores the challenges and opportunities of this integrated approach, offering policy recommendations and future research directions for truly optimized ZCSG implementation.

Spiral of Silence: Consumer Attention, Media Emotion, and Pork Price Fluctuations

This study utilized web crawling and text analysis to collect weekly data from July 2018 to March 2022. Using the spiral of silence theoretical framework, it explores the relationship between consumer online attention during sudden animal epidemics and pork price volatility. The research found that consumer online attention, influenced by animal disease outbreaks, significantly intensifies pork price volatility both directly and indirectly through changes in media sentiment and consumer expectations. On the one hand, the sentiment in official media reports partially mediates the relationship between consumer attention and pork price volatility. As consumer attention increases, positive sentiment in official media also rises, exerting an indirect stabilizing effect on pork prices. On the other hand, consumer expectations also have a partial mediating role, and the volume of negative news reports from unofficial media significantly amplifies the impact of consumer attention on pork price volatility. These findings provide valuable insights into the relationship between consumer online attention and market fluctuations in the agricultural sector and offer practical recommendations for enhancing demand-side management strategies.

Analisis Teknis dan Keekonomian Sistem Hibrida Pada Pelayanan Kelistrikan Pulau Rhun

Indonesia, as the largest archipelagic country, faces significant challenges in providing consistent electricity to its numerous isolated microgrids, many of which rely on diesel generators. The limited operational hours of these systems hinder economic growth, especially in remote regions like Rhun Island. This research aimed to develop an optimized hybrid power system integrating photovoltaic (PV) panels and Battery Energy Storage Systems (BESS) to extend electricity availability and reduce reliance on fossil fuels. Using simulation tools such as PVsyst and HOMER Pro, the study evaluated various configurations to identify the most cost-efficient and sustainable solution. Additionally, Demand Side Management (DSM) strategies, including peak clipping and load shifting, were applied to further optimize the system's performance and reduce operational costs. The findings demonstrated that the hybrid system significantly decreased the Levelized Cost of Energy (LCOE) to $0.431/kWh compared to a diesel-only system. Furthermore, the renewable energy fraction increased to 81.7%, contributing to Indonesia's energy transition goals. This research offers a replicable model for other small islands, promoting sustainable energy solutions and supporting local economic development by ensuring a stable 24-hour power supply. The implications suggest that adopting hybrid systems with DSM can accelerate energy equity and environmental sustainability in remote areas.

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.