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Power distribution networks at the distribution level are becoming more complex in their behavior and more heavily stressed due to the growth of decentralized energy sources. Demand response (DR) programs can increase the level of flexibility on the demand side by discriminating the consumption patterns of end-users from their typical profiles in response to market signals. The exploitation of artificial intelligence (AI) methods in demand response applications has attracted increasing interest in recent years. Particle swarm optimization (PSO) is a computational intelligence (CI) method that belongs to the field of AI and is widely used for resource scheduling, mainly due to its relatively low complexity and computational requirements and its ability to identify near-optimal solutions in a reasonable timeframe. The aim of this work is to evaluate different PSO methods in the scheduling and control of different residential energy resources, such as smart appliances, electric vehicles (EVs), heating/cooling devices, and energy storage. This review contributes to a more holistic understanding of residential demand-side management when considering various methods, models, and applications. This work also aims to identify future research areas and possible solutions so that PSO can be widely deployed for scheduling and control of distributed energy resources in real-life DR applications.

The exponential deployment of electric vehicles (EVs) in the residential sectors in recent years allows better energy utilization in the decentralized and centralized levels of distribution systems due to their bidirectional operation and energy storage capabilities. However, to execute these, it is necessary to adopt residential demand side management (RDSM) to schedule energy utilization effectively to fetch economical and efficient energy consumption and grid stability and reliability, particularly during peak load conditions. The paper aims to formulate a robust and efficient RDSM technique to provide an energy utilization scheduling considering various influential factors and critical roles of EVs in RDSM. A Binary Whale Optimization Algorithm (BWOA) approach is proposed as an efficient algorithm for EV’s impact on the RDSM for better energy scheduling. A single-objective formulation is presented with detailed modelling considering economic energy utilization as the primary objective with all possible equality and inequality system operational constraints. Secondly, the impact of EVs on the RDSM is studied from various perspectives in result analysis, considering EVs as load, storage devices, and different bidirectional modes of operation with other vehicles, residential components, and grids. In addition, the EVs role and the mutual influence with the integration of renewable energy sources (RES) and energy storage devices (ESDs) are extensively analyzed to provide better residential energy management (REM) in terms of economic, environmental, robust, and reliable points of view. The load priority based on consumer choice is also incorporated in the formulation. Extensive simulation is done for the proposed approach to show the effect of EVs on REM, and the results are impressive to show the EV’s role as a load, as a storage device, and as a mutually supportive device to RES, ESD, and grid.

This study proposes a new demand‐side management (DSM) technique, which is characterised by low computational requirements. The proposed technique relies on developing an operational matrix by the device local controller based on the device characteristics and the customer preferences. This matrix is sent to the energy management system (EMS) without the need to send any further information about the device or the customer preferences; then, the EMS chooses the optimal schedule for the device. To demonstrate the effectiveness of the proposed DSM technique, it is incorporated in an EMS that consists of three units controlled by a centralised microgrid controller (MGC). The three units managed by the MGC are the data collection and storage engine, the forecasting engine, and the optimisation engine. The EMS utilises the rolling horizon concept to manage real‐time information and to provide the plug‐and‐play option for all controllable devices. Simulation results on a typical microgrid system show that the proposed DSM technique outperforms conventional DSM approaches in terms of the computational time.

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 investigates the effectiveness of an Optimal Time Recommendation model (OTR) in encouraging citizens to shift the usage of their home appliances, such as dishwasher to off-peak hours. The research was conducted at the HSB Living Lab + in Gothenburg city, involving 74 participants from diverse social groups, including students, one-person households, couples, and families with kids. The study employed a mixed-methods approach, combining surveys, interviews, and data from self-reporting QR-code or iPad-based web-interface. Participants were provided with personalised recommendations generated by the OTR model, which considered factors such as energy demand, grid load, electricity pricing and level of CO2. The recommendations aimed to assist users in identifying the optimal time slots for operating their home appliances during off-peak, motivated by the lower price, lower CO2 emission or both. Results indicated a positive response from participants across all social groups. Most participants reported an increased awareness of their energy consumption patterns and a willingness to adopt delay shifting practices. However, some frictions and obstacles to adopt shifting time of the behaviour were highlighted as well. The findings from this case study contribute to the existing knowledge on flexibility and Demand-Side Management (DSM). These findings can inform home appliances producers to increase the delay start function usability, policymakers to emphasise the eco-design of the white goods, and researchers in developing effective strategies to encourage energy conservation practices on a larger scale.

This study presents a novel fully distributed and cooperative demand side management framework based on adaptive diffusion strategy. In this approach, each customer autonomously and without any need for the global information, minimises his incommodity function. The proposed framework has ability to track drifts resulting from the changes in the customer preferences and conditions or any rapidly changing price parameter coming from the wholesale market. In this scenario, the customers aim at maximising their individual utility functions; while the utility company aims at minimising the smart grid total payment (i.e. maximisation of the social welfare). The authors show that there is no need for the utility company to participate in the scheduling program for maximising social welfare. This measurement is maximised adaptively when the customers minimise their incommodity. Moreover, the authors provide a detailed analysis of the robustness of the proposed strategy in the presence of imperfect communication/computation conditions. Numerical results show that the proposed framework performs well, is scale free, and can achieve lower peak‐to‐average ratio of the total energy demand compared with that achieved by the game theoretical methods.

The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting.

International policies and targets to globally reduce carbon dioxide emissions have contributed to increasing penetration of distributed energy resources (DER) in low‐voltage distribution networks. The growth of technologies such as rooftop photovoltaic (PV) systems and electric vehicles (EV) has, to date, not been rigorously monitored and record keeping is deficient. Non‐intrusive load monitoring (NILM) methods contribute to the effective integration of clean technologies within existing distribution networks. In this study, a novel NILM method is developed for the identification of DER electrical signatures from smart meter net‐demand data. Electrical profiles of EV and PV systems are allocated within aggregated measurements including conventional electrical appliances. Data from several households in the United States are used to train and test classification and regression models. The usage of conventional machine learning techniques provides the proposed algorithm with fast processing times and low system complexity, key factors needed to differentiate highly variable DER power profiles from other loads. The results confirm the effectiveness of the proposed methodology to individually classify DER with performance metrics of 96% for EV and 99% for PV. This demonstrates the potential of the proposed method as an embedded function of smart meters to increase observability in distribution networks.

The energy demand in buildings represents a considerable share of the overall energy use. Given the significance and acknowledged flexibility of thermostatically controlled loads, they represent an interesting option for the implementation of demand side management (DSM) strategies. In this paper, an overview of the possible DSM applications in the field of air conditioning and heat pumps is provided. In particular, the focus is on the heat pump sector. Three case studies are analyzed in order to assess the energy flexibility provided by DSM technologies classified as energy efficient devices, energy storage systems, and demand response programs. The load shifting potential, in terms of power and time, is evaluated by varying the system configuration. Main findings show that energy efficient devices perform strategic conservation and peak shaving strategies, energy storage systems perform load shifting, while demand response programs perform peak shaving and valley filling strategies.

Advances in the introduction of fluctuating renewable energies, such as photovoltaics (PV), have caused power-system destabilization. However, stability can be improved if consumers change the way they use power, moving to time slots when the PV output in an area is high. In large cities in developed countries, where the types of distributed energy resources are varied, demand side management (DSM) in which consumers share power supplies and adjust the demand has received considerable attention. Under effective DSM that uses the latest information and communication technology to maximize the use of renewable energy, we believe that sparing use of appliances is not the only solution to address global warming. If behavioral change shifts the use of domestic appliances from one time slot to other time slots, we do not have to abandon the use of these appliances. The aim of this study is to determine the possibility of such behavioral changes in people in order to provide basic information for operating an effective DSM. To that end, we conducted a questionnaire-based survey of 10,000 households in Japan. We investigated the proportion of people responding to a request for a demand response (DR) under the given presented reward in time slots when DSM by DR is required. We also analyzed the factors influencing people’s response to a request for a DR. Furthermore, based on the rewards likely to be achieved in the adjustable power market, we estimated how much adjustable power would be realized.