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The concept of demand-side management (DSM) was invented in the late 1970s along with the development of many of the frameworks in use to plan and implement it in the years immediately following. It was originally referred to as demand-side load management. It is generally defined as the planning and implementation of those activities designed to influence consumer use of electricity in ways that will result in changes in the utility’s load shape—i.e., changes in the time pattern and magnitude of a utility’s load. This paper describes the evolution it has undergone since its invention and some likely changes ahead. DSM largely originated in the U.S., but is practiced in various forms through the world today. This paper uses U.S. data as examples.

Air-conditioner (AC) accounts for a significant share of residential energy consumption. Considering the widespread rise in electric vehicle (EV) usage, its charging would also contribute a considerable percentage of consumer's total energy consumption. Consequently, the concurrent operation of AC and EV charging would result in peaky load curves. Hence, this study proposes a system of agents for AC and EV charging applications, which incorporates load-management strategies to flatten the load curve. Thereby, the presented system includes two agents, namely: a smart load node for a thermostatically controlled load (SLN-TCL) and a smart battery charge controller. Subsequently, a subagent, namely micro-node, has been introduced to support SLN-TCL and to implement the concept of distributed temperature sensing (DTS). The implementation of DTS subdues the conventional temperature sensing mechanism of AC and ensures a more flexible operation. This study includes the design, development, and features of agents and subagents for AC and EV applications. Furthermore, this study also demonstrates the agent-based control actions for peak-shaving under real conditions to showcase the performance of this system.

Electricity demand has grown over the past few years and will continue to grow in the future. The increase in electricity demand is mainly due to industrialization and the shift from a conventional to a smart-grid paradigm. The number of microgrids, renewable energy sources, plug-in electric vehicles and energy storage systems have also risen in recent years. As a result, future electricity grids have to be revamped and adapt to increasing load levels. Thus, new complications associated with future electrical power systems and technologies must be considered. Demand-side management (DSM) programs offer promising solutions to these issues and can considerably improve the reliability and financial performances of electrical power systems. This paper presents a review of various initiatives, techniques, impacts and recent developments of the DSM of electrical power systems. The potential benefits derived by implementing DSM in electrical power networks are presented. An extensive literature survey on the impacts of DSM on the reliability of electrical power systems is also provided for the first time. The research gaps within the broad field of DSM are also identified to provide directions for future work.

This study proposes a local and decentralised load management procedure to provide frequency control support in power systems, without a complex communication network. In this context, the authors aim is to design and develop a new and innovative control algorithm using iterated mappings. Iterated mappings are obtained by local frequency measurements and are proved to be strong tools in dynamical deterministic systems’ analysis. The proposed algorithm is capable of assigning relevant operational states to power system during power contingencies; control action will then be exerted on that basis, with a simple and easy to implement data processing procedure. Efficiency of the control method is then verified by simulations in MATLAB environment.

Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies.

This paper examines the issue of load management in electrical distribution networks, with a focus on the optimal placement of telecommunication load breaker switches (TCLBSs). The main innovation of this study lies in the introduction of a dual‐objective optimization framework that simultaneously aims to minimize energy not supplied (ENS) and preserve critical loads. This approach enables network operators to prioritize switch placement based on the importance of each objective, offering a level of flexibility, and comprehensiveness not thoroughly addressed in previous studies. To solve this problem, an iterative graph‐based search algorithm is employed, which dynamically identifies the optimal locations for switch installation. By leveraging graph theory, the proposed method significantly reduces computational complexity and enhances responsiveness to changing network conditions. According to simulation results on the IEEE 33‐bus network, switch locations change as the priority of critical loads increases, and the proposed strategy greatly improves the reliability and performance of the power distribution network during peak demand periods. This dual‐objective approach, with adjustable settings, enhances the efficiency of load management, and strengthens network dependability.

Microgrids are an emerging technology that offers many benefits compared with traditional power grids, including increased reliability, reduced energy costs, improved energy security, environmental benefits, and increased flexibility. However, several challenges are associated with microgrid technology, including high capital costs, technical complexity, regulatory challenges, interconnection issues, maintenance, and operation requirements. Through an in-depth analysis of various research areas and technical aspects of microgrid development, this study aims to provide valuable insights into the strategies and technologies required to overcome these challenges. By assessing the current state of microgrid development in Pakistan and drawing lessons from international best practices, our research highlights the unique opportunities microgrids present for tackling energy poverty, reducing greenhouse gas emissions, and promoting sustainable economic growth. Ultimately, this research article contributes to the growing knowledge of microgrids and their role in addressing global sustainability issues. It offers practical recommendations for policymakers, industry stakeholders, and local communities in Pakistan and beyond.

Energy-efficient retrofits embrace enhancement of the building envelope through climate control strategies, employment of building-integrated renewable energy technologies, and insulation for a sustainable city. Building envelope improvements with insulation is a common approach, yet decision-making plays an important role in determining the most appropriate envelope retrofit strategy. In this paper, the main objective is to evaluate different retrofit strategies (RS) through a calibrated simulation approach. Based on an energy performance audit and monitoring, an existing building is evaluated on performance levels and improvement potentials with basic energy conservation measures. The considered building is experimentally monitored for a full year, and monitoring data are used in calibrating the simulation model. The validation of the base model is done by comparing the simulation analysis with the experimental investigation, and good agreement is found. Three different retrofit strategies based on Intervention of minor (RS1), Moderate (RS2), and Major (RS3) are analyzed and juxtaposed with the base model to identify the optimal strategy of minimizing energy consumption. The result shows that total energy intensity in terms of the percentage reduction index is about 16.7% for RS1, 19.87 for RS2, and 24.12% for RS3. Hence, RS3 is considered the optimal retrofit strategy and is further simulated for a reduction in carbon dioxide (CO2) emissions and payback investigation. It was found that the annual reduction in CO2 emissions of the building was 18.56%, and the payback period for the investment was 10.6 years.

The load shifting strategy is a form of demand side management program suitable for increasing the reliability of power supply in an electrical network. It functions by clipping the load demand that is above an operator-defined level, at which time is known as peak period, and replaces it at off-peak periods. The load shifting strategy is conventionally performed using the preventive load shifting (PLS) program. In this paper, the corrective load shifting (CLS) program is proven as the better alternative. PLS is implemented when power systems experience contingencies that jeopardise the reliability of the power supply, whereas CLS is implemented only when the inadequacy of the power supply is encountered. The disadvantages of the PLS approach are twofold. First, the clipped energy cannot be totally recovered when it is more than the unused capacity of the off-peak period. The unused capacity is the maximum amount of extra load that can be filled before exceeding the operator-defined level. Second, the PLS approach performs load curtailment without discrimination. This means that load clipping is performed as long as the load is above the operator-defined level even if the power supply is adequate. The CLS program has none of these disadvantages because it is implemented only when there is power supply inadequacy, during which the amount of load clipping is mostly much smaller than the unused capacity of the off-peak period. The performance of the CLS was compared with the PLS by considering chronological load model, duty cycle and the probability of start-up failure for peaking and cycling generators, planned maintenance of the generators and load forecast uncertainty. A newly proposed expected-energy-not-recovered (EENR) index and the well-known expected-energy-not-supplied (EENS) were used to evaluate the performance of proposed CLS. Due to the chronological factor and huge combinations of power system states, the sequential Monte Carlo was employed in this study. The results from this paper show that the proposed CLS yields lower EENS and EENR than PLS and is, therefore, a more robust strategy to be implemented.

The installation of solar photovoltaics (PV) has gained momentum due to growing concerns about global warming and the UN’s SDGs addressing environmental challenges. The primary objective of this paper is to evaluate and address the impacts of load uncertainty on Unit Commitment through the implementation of storage-based PV generation, wherein PV generation and energy storage operate in the proposed coordinated manner. To deal with uncertainty, a hybrid optimization technique is utilized, which combines stochastic and robust computations. Stochastic load uncertainty scenarios are generated via probabilistic Gaussian Probability Density function (PDF) approach that reside within the defined uncertainty set, as established by the robust optimization framework. The mean scenario of load uncertainty is applied to evaluate the day-ahead UC costs. The IEEE 39-bus, ten-generator system serves as the basis for this analysis. UC is optimized via Dynamic Programming (DP) in the presence of load uncertainty levels of up to 10% across three distinct case studies. Case 1 functions as the baseline for comparison as it does not include PV-storage or load uncertainty modeling. In Case 2, the influence of load uncertainty on day-ahead UC is examined for a network that excludes PV-storage. In Case 3, the system integrates the proposed coordination based PV-storage and solves UC while managing peak demand amid increasing levels of load uncertainty—specifically at 5%, 8%, and 10%. Additionally, contingency margins are evaluated across all three cases to validate day ahead 24 hours system performance and reliability enhancements. By juxtaposing the results of UC across these three cases, this study aims to analyze the implications of gradually increasing load uncertainty, load management, and peak load regulation utilizing PV-storage systems.