Effective Demand Side Management Research Articles

Applying the Eightfold Path to Morocco's Cabinet Order 2.23.105

ABSTRACTMorocco is fighting its sixth year of drought. As part of its comprehensive response to the drought, the Moroccan cabinet passed order 2.23.105 in December 2023. The new rules require the regulation of well drilling entities. The problem of illegal wells, often dug by farmers who do not know that they need a license, represented a threat to the country's water tables long before the current drought and constitutes a significant barrier towards the adoption of effective Integrated Water Resource Management and Demand Side Management policies. The paper is based on an examination of the current laws including order 2.23.105, by applying the eightfold path of policy analysis, interviews with water officials, and including the views of farmers concerning their efficacy. The paper argues that the state is an essential player in framing an effective response to drought and to climate change. Soil and Water Analysis Tools (SWAT) like ArcSWAT will help improve the state's response and enable it to identify and engage key stakeholders.

Estimation of Domestic Load Profile for Effective Demand-Side Management

In this article domestic base and shift based load profile are estimated for effective Demand Side Management (DSM). Domestic electricity consumption in Pakistan accounts for approximately 40% of total electricity sales. Effective DSM can reduce electricity consumption. Power generation companies are increasingly recognising the importance of analysing their customers' consumption patterns. This article proposes an agent-based modelling approach for forecasting household electricity consumption profile. AnyLogic software is used for establishing the model that uses variables and functions to estimate base and shiftable load of three hundred households. This article also estimates the weekly and 24- hour load profile for effective DSM operations by smart grid infrastructure. The results show that the agent-based simulation closely approximates real-time data, with a difference of only 2.4%.

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.

Optimizing battery energy storage prototypes for improved resilience in commercial buildings: Gaussian mixture modeling and hierarchical analysis of energy storage potential

The increasing energy demand in commercial buildings has led to the adoption of photovoltaic (PV) systems as a viable solution. However, the gap between energy consumption and PV capacity in commercial buildings remains uncertain, and there is limited research on the potential for energy storage in different commercial building types. This study utilizes Gaussian mixture modeling to extract energy consumption and PV capacity patterns from diverse commercial buildings, using datasets provided by the Department of Energy. By employing hierarchical analysis, we investigate the energy storage potential of various commercial buildings based on the disparities between energy consumption and PV capacity patterns. Moreover, this study introduces innovative battery energy storage system (BESS) prototypes tailored to the specific needs of different commercial building types. These prototypes determine the optimal BESS capacity and charging schedules, providing an effective means of improving electrical resilience in commercial buildings. The findings reveal that medium offices, warehouses, and secondary schools require BESS capacities of 410kWh, 838kWh, and 349kWh, respectively, and recommend charging time windows of 9:00–17:00, 8:00–18:00, and 9:00–14:00, respectively. Additionally, the cost recovery period for Li-ion phosphate and ternary Li-ion batteries ranges from approximately 2.5 to 7.5 years and 4 to 8.2 years, respectively. The developed BESS prototypes based on energy storage potential offer resilience to commercial buildings during power outages caused by natural disasters and emergencies, helping to improve their electrical resilience. This study provides valuable insights into the potential for energy storage in commercial buildings and promotes the wider implementation of BESS in the commercial sector, contributing to the achievement of effective demand-side management.

Optimal coal power phase-out pathway considering high renewable energy proportion: A provincial example

Since the announcement of the “carbon neutrality” goal, China has actively promoted the low-carbon transition across various industries. Controversy surrounds the fate of existing coal power units as, despite their carbon emissions, they remain integral to meeting the escalating power demand. Simultaneously, the integration of renewable energy sources (RES) underscores the need for coal power to provide flexible services, ensuring the safety and stability of the power grid. This study takes a province as example, establishing a comprehensive simulation model that considers a high share of RES to evaluate the optimal phase-out pathway for coal power in China. The analysis incorporates factors such as Carbon Capture and Storage (CCS), Battery Energy Storage System (BESS), and power demand growth. Results suggest that an immediate reduction in coal power capacity is not viable due to concerns about power supply safety. Recommendations emphasize the importance of maintaining a steady coal power capacity in the short term while concurrently developing RES, BESS, and CCS technologies. This approach facilitates a judicious withdrawal of coal power, contributing to cost reduction and ensuring a balanced transition. Furthermore, effective demand-side management emerges as a crucial.

Residential responses to service-specific electricity demand: Case of China

Understanding the diversity of the residential demand for various electrical services is critical for utilities and policymakers in conducting effective demand side management and narrowing urban-rural inequality. Previous research has usually treated the household as a unit of analysis, and thus may have ignored the fact that household electricity consumption is derived demand driven by specific services, which fails to examine the heterogeneous behavioral responses. Therefore, this paper presents a new pattern of residential demand for various electrical services and quantifies the impacts of socioeconomic determinants in China. The conditional demand analysis is performed on the unique dataset of the Chinese Residential Energy Consumption Survey of 2014 to estimate the electricity demand distribution in eight types of services and to investigate the effect of socioeconomic variables on service-specific electricity consumption. The results show that, together, entertainment and food refrigeration account for about half of the total annual electricity consumption, followed by laundry, lighting, space cooling, and hot water. Rural households use about 7.2% of total electricity for cooking purposes, while urban counterparts hardly use electricity to cook at all. Electricity consumption for space heating is negligible for both urban and rural households. Heterogeneity in socioeconomic determinants is found not only among different electrical services but also between urban and rural households.

How to make sustainable water-saving policy based on public preferences in China? A conjoint analysis perspective

Scientific and effective demand-side management policies need to be based on practitioners' preferences to cope with the increasingly severe water shortage, but little attention has been paid to this topic. The paper studies public preferences for attributes of the 22 daily water-saving measures in the domestic sector in China by using conjoint analysis and unveils the heterogeneity in preferences by using variance analysis. The first-hand survey data and statistical analysis show that activities with large water-saving potential is less acceptable; the group with higher affordability has lower acceptance of technical water-saving measures; water-saving measures in public places with great water-saving potential are less acceptable than home measures. The gap between unveiled and socially desirable public preferences constitutes a pivotal barrier that need to be carefully considered in formulating scientific sustainable water-saving policies.

Real-Time Load Variability Control Using Energy Storage System for Demand-Side Management in South Korea

In today’s power systems, the widespread adoption of smart grid applications requires sophisticated control of load variability for effective demand-side management (DSM). Conventional Energy Storage System (ESS)-based DSM methods in South Korea are limited to real-time variability control owing to difficulties with model development using customers’ load profiles from sampling with higher temporal resolution. Herein, this study thus proposes a method of controlling the variability of customers’ load profiles for real-time DSM using customer-installed ESSs. To optimize the reserved capacity for the proposed maximum demand control within ESSs, this study also proposes a hybrid method of load generation, which synthesizes approaches based on Markov Transition Matrix (MTM) and Artificial Neuron Network (ANN) to estimate load variations every 15 min and, in turn reserve capacity in ESSs. The proposed ESS-based DSM strategy primarily reserves capacity in ESSs based on estimated variation in load, and performs real-time maximum demand control with the reserved capacity during scheduled peak shaving operations. To validate the proposed methods, this study used load profiles accumulated from industrial and general (i.e., commercial) customers under the time-of-use (TOU) rate. Simulation verified the improved performance of the proposed ESS-based DSM method for all customers, and results of Kolmogorov-Smirnov (K–S) testing indicate advances in the proposed hybrid estimation beyond the stand-alone estimation using the MTM- or ANN-based approach.

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium's Niagara Framework for Residential Demand-Side Management.

Electricity is a vital resource for various human activities, supporting customers’ lifestyles in today’s modern technologically driven society. Effective demand-side management (DSM) can alleviate ever-increasing electricity demands that arise from customers in downstream sectors of a smart grid. Compared with the traditional means of energy management systems, non-intrusive appliance load monitoring (NIALM) monitors relevant electrical appliances in a non-intrusive manner. Fog (edge) computing addresses the need to capture, process and analyze data generated and gathered by Internet of Things (IoT) end devices, and is an advanced IoT paradigm for applications in which resources, such as computing capability, of a central data center acted as cloud computing are placed at the edge of the network. The literature leaves NIALM developed over fog-cloud computing and conducted as part of a home energy management system (HEMS). In this study, a Smart HEMS prototype based on Tridium’s Niagara Framework® has been established over fog (edge)-cloud computing, where NIALM as an IoT application in energy management has also been investigated in the framework. The SHEMS prototype established over fog-cloud computing in this study utilizes an artificial neural network-based NIALM approach to non-intrusively monitor relevant electrical appliances without an intrusive deployment of plug-load power meters (smart plugs), where a two-stage NIALM approach is completed. The core entity of the SHEMS prototype is based on a compact, cognitive, embedded IoT controller that connects IoT end devices, such as sensors and meters, and serves as a gateway in a smart house/smart building for residential DSM. As demonstrated and reported in this study, the established SHEMS prototype using the investigated two-stage NIALM approach is feasible and usable.

Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

Smart meters with automatic meter reading functionalities are becoming popular across the world. As a result, load measurements at various sampling frequencies are now available. Several methods have been proposed to infer device usage characteristics from household load measurements. However, many techniques are based on highly intensive computations that incur heavy computational costs; moreover, they often rely on private household information. In this paper, we propose a technique for the detection of appliance utilization patterns using low-computational-cost algorithms that do not require any information about households. Appliance utilization patterns are identified only from the system status behavior, represented by large system status datasets, by using dimensionality reduction and clustering algorithms. Principal component analysis, k-means, and the elbow method are used to define the clusters, and the minimum spanning tree is used to visualize the results that show the appearance of utilization patterns. Self organizing maps are used to create a system status classifier. We applied our techniques to two public datasets from two different countries, the United Kingdom (UK-DALE) and the US (REDD), with different usage patterns. The proposed clustering techniques enable effective demand-side management, while the system status classifier can detect appliance malfunctions only through system status analyses.

Consumer satisfaction-oriented residential appliance scheduling algorithms

Through effective demand side management, residential energy consumers can enjoy both lower electricity bills and satisfactory electricity-consuming experiences. Lots of residential appliance scheduling algorithms have been proposed, and consumer satisfaction has attracted more and more attention. In this paper, the consumer satisfaction index is defined, and AHP (Analytic Hierarchy Process) is applied to describe the consumer sensitivity to the usage time of each appliance. The time and appliance models are constructed, by use of which the minimum electricity bill model is set up. Based on these models, the consumer satisfaction-oriented residential appliance scheduling algorithm is developed. A case study shows that the new algorithm can help consumers cut down the electricity bill and use the appliances at their favourite time slots. Compared with the consumer's original energy usage scheme, the optimized scheme reduces the electricity bill by 33.56%. At the same time, the Peak-to-Average Ratio can be lowered by 5.26%.

A novel billing approach for fair and effective demand side management: Appliance level billing (AppLeBill)

Demand side management (DSM) is an important part of smart electrical grid studies due to high energy efficiency it provides. Effective DSM techniques are hinged on participation level of the customers and control manner of their loads so if there is no fair and appropriate management, customers will not embrace demand side management applications. Besides, the customers might not participate to the demand response process willingly if their comforts are not considered. Therefore, the DSM systems must consider fairness and customer’s comfort as well as distribution system objectives. Multifarious DSM systems have been proposed in the literature, however, the fair billing has not included as a parameter in most of these studies. This paper introduces a novel billing approach based upon appliance level billing (AppLeBill) for fair and effective demand side management. The proposed AppLeBill idea offers a billing strategy such that time-shiftable appliances (TSAs) in a residence may have a separate billing rate rather than a single billing rate for total electricity usage. The proposed AppLeBill concept includes three different schemes which are explained and simulated in order to compare each other in terms of their potential benefits.

Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA.

THE METERING SYNCHRONIZATION IN THE LIBERALIZED ELECTRICITY MARKETS

The differentiated over time periods tariffs application has the purpose not only of calculating for the electricity, but primarily an effective demand side management tool by stimulating consumers to energy efficiently usage. In liberalized markets, the cost of electricity is differentiated on an hourly basis, which requires players from the market to agree on modes of electric energy production and consumption in real time. An integral condition for the implementation of energy-efficient behavior in terms of time-differentiating electricity costs is, inter alia, synchronization of electric energy metering at every point of its flow in the market and the binding of the metering results to the national time scale. The article analyzes the errors arising from the scan synchronization of electricity metering, the allowable metering of scan synchronization at each level of the distributed smart system for commercial electricity metering of the electricity market of Ukraine is estimated, and the technical solutions for ensuring the metering synchronization in the liberalized electricity markets are proposed.

Development of integrated demand and supply side management strategy of multi-energy system for residential building application

The multi-energy system that contains the highly coupled energy supply equipment units can be adopted to simultaneously satisfy the cooling, heating and electrical energy demands. Owing to the complex nature of multiple supplies and demands, an integrated demand and supply side management strategy was proposed for aligning the system operation and building energy demands. The proposed strategy included three core algorithms: demand side rolling optimization, supply side rolling optimization and feedback correction. Demand and supply side rolling optimizations were simultaneously implemented to determine the schedulable appliances and operating capacities of energy supply equipment units over the next 24-h planning horizon. Feedback correction was involved for continual modification on any discrepancy between various actual and predicted energy demands. In this study, a trigeneration system primed with solid oxide fuel cell-gas turbine was designed as the multi-energy system to serve a high-rise apartment building with electric cars. It was found that the primary energy consumption, the design capacity of the prime mover and the capacity of electricity storage would be decreased by 8.75%, 10% and 43% respectively through the integrated demand and supply side management strategy. It was also noted that electric cars played important roles in effective demand side management.

Towards demand side management control using household specific Markovian models

We devise a real-time control mechanism to aid electricity network operators facilitate effective residential Demand Side Management. Contrary to existing algorithms where individual customer behavior patterns are required or estimated, we propose a novel load management scheme for highly stochastic loads where each household possesses individually suited parameters. If the actual demand in a feeder is found to exceed its rating, selected customers are requested by the operator to lower their loads. The benefit of the proposed approach is to maximize the certainty of meeting Demand Response targets whilst minimizing the overall cost associated with the Demand Response control actions. Our contribution is a model that takes customer behavior patterns into consideration while selecting customers. We model load behavior of individual households using Markov chains and by treating the problem as a Markov Decision Process optimal control problem, we deduce an easily implementable strategy for load reduction. This strategy is then incorporated in an aggregate model that utilizes household specific transition probabilities. The practical application is explained based on real data. Our numerical results illustrate the virtue of the strategy, showing potential for the use of household specific Markovian models for Demand Side Management applications.

Multi-Objective Day-Ahead Scheduling of Power Market Integrated With Wind Power Producers Considering Heat and Electricity Trading and Demand Response Programs

The large-scale penetration of renewable energy, such as wind power, brings a lot of economic and environmental benefits to the grid, and it also causes hidden dangers in the reliability and security of the power system due to its uncertainty. As an effective demand-side management method, demand response has unique advantages in smoothing wind power fluctuations and mitigating grid pressure. This paper proposes a new model for the demand response aggregator (DRA) that includes both combined heat and power systems (CHPS) and energy storage devices. DRA can interact with the Independent system operator (ISO) through combined heat and power (CHP) units, energy storage devices, and the heat buffer tank to benefit from the electricity market and the thermal market simultaneously. At the same time, wind power producers (WPP) are modeled to turn wind power that was initially passively consumed into active market participants. The problem is modeled using an improved weighted method, which aims to take the diverse objectives of multiple market participants into account. The proposed model is tested on the modified IEEE RTS-24 test system to analyze the optimal scheduling strategies of each participant in the power market.

Delay-tolerant distributed voltage control for multiple smart loads in AC microgrids

With increasing penetration of variable loads and intermittent distributed energy resources (DERs) with uncertainty and variability in distribution systems, the power system gradually inherits some features (e.g., lack of rotating inertia), which leads to the voltage instability in microgrids. As a means to provide stability support for smart grid against high penetration of intermittent DERs, inverter-based smart loads across the distribution grid has been suggested recently. Accordingly, this paper presents a delay-tolerant distributed voltage control scheme based on consensus protocol for multiple-cooperative smart loads through effective demand-side management in ac microgrids, in which the time-delay effect on transmission communication occurred in information exchanges is considered. The proposed distributed voltage control scheme always enables the output voltage of each smart load to be synchronized to their reference value, which improves the robustness of system stability against transmission communication delays. The Lyapunov–Krasovskii functions are employed to analyze the stability of our proposed distributed control scheme, then the delay-independent stability conditions are derived, which allows some large communication delays. Moreover, the sensitivity analysis is developed to show how the time delay affects system dynamics in order to validate the robustness of proposed delay-independent stability conditions. Furthermore, a sparse communication network is employed to implement the proposed distributed control protocols, which thus satisfies the plug-and-play requirement of smart microgrids. Finally, the simulation results of an ac microgrid in MATLAB/SimPowerSystems are presented to demonstrate the effectiveness of the proposed control methodology.

Heterogeneity and persistence in the effect of demand side management stimuli on residential gas consumption

Feedback on residential energy consumption has long been identified as an effective demand side management (DSM) instrument to encourage household energy conservation. This paper explores the heterogeneous treatment effects of a DSM program on residential gas consumption across different groups of households categorised by their socio-economic and household characteristics. The program was employed in Ireland's Smart Metering Gas Consumer Behavioural Trial and the demand stimuli tested consisted of informational feedback on a household's gas usage. The paper also investigates the impact of the stimuli over time and across the distribution of daily household gas consumption. The demand stimuli are found to have very different effects across the socio-economic and dwelling characteristics of the households with older and larger households and dwellings revealed to be much more responsive to the feedback. Additionally, the results provide evidence that the impacts are persistent over time.