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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.

Demand Side Management (DSM) is a well-recognized concept that seeks to optimize the efficiency and effectiveness of a distribution system. DSM encompasses load shifting and load curtailment strategies, both designed to mitigate the system's peak demand. The former is an optimization-based method that repositions the elastic loads to hours with low tariff rates, thereby filling the gaps and reducing the peak. The latter offers incentives to consumers to encourage their participation and reduce energy consumption during periods of high demand. In order to minimize the overall operating cost, the unique work done in this study intends to analyze ten exhaustive cases on a low voltage (LV) microgrid (MG) system and optimally schedule the distributed energy resources (DERs). The complexity of the work is further enhanced by the inclusion of plug-in hybrid electric vehicle (PHEV) which integrates Grid to Vehicle (G2V) and Vehicle to Grid (V2G) technologies to charge and discharge itself using the utility assigned electricity market price. The work used the Differential Evolution (DE) method as an optimisation framework. The ten exhaustive scenarios were analyzed to acknowledge the impact of the involvement and pricing of the grid, PHEV and DSM strategies mentioned above. Numerical study confirms that the load shifting policy and the type of load curtailing policy which rewarded the customer based on their willingness to curtail loads along with delivering benefit to the DISCOM were more cost-effective compared to the rest of the cases. Furthermore, it was also noted that time of usage (TOU) based electricity pricing was economical for entities which participated in bidirectional flow of power like grid and PHEV.

As the need to reduce use in the building sector increases, thermally activated building systems (TABS) have gained attention for providing both comfort and energy efficiency. Their large thermal mass enables peak load shifting, making them suitable for demand response (DR). Effective DR control requires methods that can flexibly handle dynamic building behavior, disturbances, and varying thermal characteristics. While model predictive control (MPC) is capable of predictive optimization, conventional MPC relies on fixed models and lacks adaptability to time‐varying system conditions. This study introduces an adaptive MPC (AMPC) method, which incorporates online estimation and sequential model updating, to realize a DR‐based control strategy for TABS. The method was evaluated through a co‐simulation framework using Dymola and MATLAB/Simulink. Results show that AMPC can perform effective precooling and stably respond to DR requests. Through multiple case studies, the method was found to leverage the thermal storage capacity of TABS to flexibly shift cooling loads. Under the examined conditions, approximately 90%–100% of peak cooling energy was shifted to off‐peak periods, while ceiling surface temperature errors were maintained within about 0.3°C. Furthermore, PMV remained within ±0.5 in all cases, demonstrating that thermal comfort can be preserved even under restricted cooling operation.

The uncoordinated charging behaviors of electric vehicles (EVs) challenge the stable operation of the grid, e.g., increasing the peak-to-valley ratio of the grid and diminishing power supply reliability. A Monte Carlo sampling method is employed to develop a charging behavior model for EVs to solve the problems raised by random charge mode. The probability densities of daily driving distance, initial charging time, charging power, and charging duration are incorporated and analyzed. The proposed model enables multiple random sample values for EVs, considering varying weather conditions and time-of-use electricity prices. For charge and discharge optimization, an EV charge and discharge scheduling model is constructed, aiming to balance multiple objective functions, including battery degradation costs, user charging costs, grid load fluctuations, and peak-to-valley differences. The weighting method is applied to transform the multi-objective framework into a single-objective comprehensive solution, facilitating the identification of optimal charge and discharge strategies. Results demonstrate that the Monte Carlo sampling can satisfactorily generate datasets with realistic characteristics on the driving range and charging initiation time of the EVs. Furthermore, the load results achieved through multi-objective optimization demonstrate that the proposed strategy effectively mitigates peak-to-valley disparities. The peak load reduction and trough load increment are 27.6% and 160.1%, respectively. Through post-peak load balancing, the average costs of each EV for daily charging and battery degradation are reduced to be 7.58 yuan and 15.68 yuan, respectively. This approach can significantly enhance the grid stability, simultaneously address the economic interests of users, and extend battery lifespan.

The general context of this proposal is represented by the energy-efficient smart home that integrates renewable energy sources such as photovoltaic panels. The objective of this article is to minimize the amount of energy consumed from the national energy grid by producer-consumers of energy from renewable sources, in their own smart homes. In order to fulfill this goal, it was necessary to estimate the amount of renewable energy produced on the day-ahead horizon and to schedule the operation of controllable consumers in a smart home. To predict the amount of energy produced, two approaches were used: the first was based on data, and used techniques specific to artificial intelligence, more specifically, multilayer perceptron and radial-basis-function neural networks, and the second was based on models. The accuracy of the short-term prediction horizon of the techniques used was evaluated with quantitative performance indicators so that the most appropriate one in relation to the goal of the article could be selected to be used in the test scenarios. The scheduling of consumer functioning was based on their classification in relation to their ability to be controlled, and on the selection from the peer-reviewed literature of an optimization algorithm which, by load shifting from a smart home, ensured the optimal fulfillment of the objective function. The selected load-shifting algorithm was then integrated into and tested on a real database. The data used were monitored for two representative days, in terms of the amount of energy from renewable energy sources produced and consumed. The load-shifting algorithm proved its effectiveness through the results obtained and which are reported in the article.

The study presents a two-level multi-objective approach for energy scheduling in a smart distribution electrical grid. The proposed energy optimization strategy combines hybrid demand management at the upper level and multi-objective functions at the lower level. The multi-objective function in lower level is designed to minimize operational costs and enhance reliability. The upper-level demand management is optimized by taking into account price signals from the upstream grid. The hybrid demand management such as load shifting and load interruption are proposed as effective approaches for consumers. The energy scheduling in both levels by improved sunflower optimization (ISFO) algorithm is solved, and fuzzy approach based on linear programming technique for multidimensional analysis of preference (LINMAP) method is proposed for finding desired solution of the multi-objective function in lower-level. The effectiveness of the electrical grid is examined on the 69-bus distribution network through the utilization of day-ahead scheduling and incorporating findings from mathematical modeling. The results of the proposed problem with demand-side optimization lead to decreasing operation cost by 2.43% and enhancing reliability index by 0.6% compared to lack of demand-side optimization.

The ecology in the Murray-Darling Basin in Australia is threatened by water scarcity due to climate change and the over-extraction and over-use of natural water resources. Ensuring environmental flows and sustainable water resources management is urgently needed. Seawater desalination offers high potential to deliver water in virtually unlimited quantity. However, this technology is energy-intensive. In order to prevent desalination becoming a driver of greenhouse gases, the operation of seawater desalination with renewables is increasingly being considered. Our study examines the optimisation of the operation of a 100% renewable energy grid by integrating seawater desalination plants and pipelines as a variable load. We use a GIS-based renewable energy load-shifting model and show how both technologies create synergy effects. First, we analyse what quantity of water is missing in the basin in the long run. We determine locations for seawater desalination plants and pipelines to distribute the water into existing storages in the Murray-Darling Basin. Second, we design a pipeline system and calculate the electricity needed to pump the water from the plants to the storages. Third, we use the combined renewable energy load-shifting model. We minimise the total cost of the energy system by shifting energy demand for water production to periods of high renewable energy availability. Our calculations show that in such a system, the unused spilt electricity can be reduced by at least 27 TWh. The electricity system's installed capacity and levelised cost of electricity can be reduced by up to 29%, and 43% respectively. This approach can provide an annual net economic benefit of $22.5 bn. The results illustrate that the expansion of seawater desalination capacity for load-shifting is economically beneficial.

When it comes to guaranteeing appropriate performance for buildings in terms of energy efficiency, the building envelope is a crucial component that must be presented. When a substance goes through a phase transition and either gives out or absorbs an amount of energy to provide useful heat or cooling, it is called a phase-change material, or PCM for short. Transitions often take place between the matter’s solid and liquid states. Buildings use PCMs for a variety of purposes, including thermal comfort, energy conservation, managing the temperature of building materials, reducing cooling/heating loads, efficiency, and thermal load shifting. Improved solutions are applied using new method and approach investigations. Undoubtedly, researching and applying PCM use in building applications can help create buildings that are more energy-efficient and environmentally friendly, while also increasing thermal comfort and consuming less energy. It provides a possible answer to the problems posed by climate change, rising energy demand in the built environment, and energy use optimisation. However, it is true that no particular research has yet been conducted to thoroughly analyse the linked PCM applications in the building industry. Thus, the principal tactics are addressed in this paper to determine current and efficient methods for employing PCMs in buildings to store thermal energy. By gathering around 50 instances from the open literature, this study conducts a thorough assessment of the up-to-date studies between 2016 and 2023 that used PCMs as thermal energy storage in building applications. As a result, this review aims to critically evaluate the PCM integration in buildings for thermal energy storage, identify a number of issues that require more research, and draw some important conclusions from the body of literature. Specifically, the building envelope roof and external wall uses of PCMs are highlighted in this research. Applications, general and desired characteristics, and PCM types and their thermal behaviour are described. In comparison to a traditional heat storage tank that simply contains water, this review indicates that a water storage tank containing 15% PCM improves heat storage by 70%. Also, less than 7 °C of internal air temperature was reduced by the PCMs in the walls, which avoided summer warming. Finally, using PCM for space cooling resulted in substantial energy savings across the various seasons.

Demand Response (DR) is proven very efficacious in load mitigation, especially in peak time period. DR benefits both consumers and system operators so that they can reduce their payment and system operating cost, respectively. The proposed cost minimization is currently used as a clearing mechanism with locational marginal pricing scheme to determine consumers' payment. These clearing and pricing mechanisms are inconsistent as the system cost is minimized, but the final payments are calculated based on marginal prices. Payment Cost Minimization (PCM) auction as a price-based clearing mechanism is envisaged to be an effective alternative to solve the issue. This paper demonstrates how to include DR in PCM mechanism to further reduce the consumers' payment. It facilitates utilizing price responsive consumers for Load Shifting DR (LSDR) in PCM auction. The optimization problem is modeled as a mixed-integer nonlinear bi-level programming. Duality theorem, Karush-Kuhn-Tucker conditions, and integer algebra are used to convert such a problem into a single-level mixed-integer linear programing problem. This problem is then solved by CPLEX solver in GAMS. The impacts of LSDR are studied using the proposed formulation to solve the clearing problem in the case studies, deriving promising numerical results.

With the deployment of renewable energy generation, home energy storage systems (HESSs), and plug-in electric vehicles (PEVs), home energy management systems (HEMSs) are critical for end users to improve the increasingly complicated energy production and consumption in the home. However, few of the previous works study the impact of different models of battery degradation cost in the optimization strategy of a comfort-based HEMS framework. In this paper, a novel scheduling algorithm based on a mixed-integer programming (MIP) model is proposed for the HEMS. Total cost minimization, peak load shifting, and residents’ thermal comfort satisfaction are combined and considered in the optimal scheduling algorithm. The impact of battery degradation costs on the charging and discharging strategy of HESS and PEV is also compared and discussed in this case study. This case study shows that the proposed optimal algorithm of HEMS not only flattens the peak load and satisfies the thermal comfort of residents but also has better flexibility and economic advantages, reducing the electricity cost by 30.84% and total cost by 24.16%. The sensitivity analysis of the parameters for the charging and discharging strategy also guarantees the lowest cost and prolongs the service life of the battery.