Demand Side Management Techniques Research Articles

A metaheuristic approach to compare different combined economic emission dispatch methods involving load shifting policy

AbstractDistributed generators (DGs), which can be traditional fossil fuel generators or renewable energy sources (RES), must be appropriately planned in order to reduce a power network’s overall generating cost. Renewable energy sources (RES) should be prioritized because they provide a clean and sustainable energy supply and are abundant in nature. Demand side management (DSM) optimizes the scheduling of flexible loads to reduce peak demand and improve the load factor, while keeping daily demand unchanged. The test system in this research employs a dependable and effective hybrid optimization tool to plan the DGs of a dynamic system in a way that matches low active power production costs with low pollutant emissions. The fitness functions used in the test system were non-linear due to the presence of the valve point effect (VPE). The costs and emissions were evaluated for various fitness functions which included involvement of wind, DSM, and different types of combined economic emission dispatch (CEED) methods. The test system’s peak demand was cut by 12% and the load factor was raised from 0.7528 to 0.85 when DSM technique was used. The generation cost has been reduced from $1,014,996 to $1,012,182 using CSAJAYA algorithm which was further reduced to $1,007,441 after incorporating DSM. Likewise, the CEEDppf was also observed to be reduced to $1,231,435 and $1,216,885 with and without DSM compared to $1,232,001 from reported literature. Numerical results show that both the cost and emission were reduced significantly using the proposed CSAJAYA compared to a long-sighted list of algorithms published in literature. Graphical Abstract

Micro Grid Integration of Distributed Power Generation Optimal Operating Methods

Microgrids, as localized energy systems comprising distributed power generation sources, offer resilience, flexibility, and efficiency to the modern power grid. Integrating various distributed energy resources (DERs) within microgrids poses challenges in optimizing their operation to ensure reliable and cost-effective energy supply. This paper presents a comprehensive review and analysis of optimal operating methods for the integration of distributed power generation within microgrids.The study explores various aspects of microgrid operation, including optimal dispatch strategies, demand-side management techniques, energy storage utilization, and renewable energy source integration. It investigates the application of advanced optimization algorithms, such as linear programming, mixed-integer linear programming, and metaheuristic algorithms, to address the complex optimization problems inherent in microgrid operation. Furthermore, the paper examines the role of smart grid technologies, advanced control systems, and communication infrastructure in facilitating the efficient operation of microgrids with distributed power generation. It discusses the importance of real-time monitoring, data analytics, and predictive modeling for enhancing decision-making processes and optimizing microgrid performance.

Economic operation of an agent-based virtual storage aggregated residential electric-heating loads in multiple electricity markets

The increasing penetration of intermittent renewable energy sources leads to a higher demand for flexible balancing service in the power system. Consequently, demand-side management techniques, such as utilizing flexible resources from residential electric-heating loads, provide a promising solution. However, due to various uncertainties such as stochastic weather and market prices, it is challenging to develop an economically efficient strategy. Hence, this paper proposes an economic operation optimization approach for residential electric-heating loads to develop optimal bidding and scheduling strategy in the multi-stage market. First, the dynamic characteristics of aggregated electric-heating loads are modeled as the virtual energy storage systems (VESS) to quantify the flexibility potential arising from the combination of building thermal inertia, user tolerance and thermal energy storage (TES). Subsequently, the joint energy and balancing service market is introduced to exploit the operating flexibility of VESS. A multi-agent deep reinforcement learning (MADRL) optimization framework is proposed to address the multi-objective problem at different decision frequency, which involves achieving lower economic costs while maintaining indoor comfort. Finally, we combine the DRL with imitation learning (IL) by introducing the behavior cloning mechanism to mitigate training process instability. It reduces the computational costs and eliminates the need for redundant additional structure. Comparative simulation experiments demonstrate the efficiency of data-driven optimization approach, which provides an adaptive, economic cost-effective and general solution for multi-type aggregators to activate their flexibility in dynamic markets.

Optimal management of microgrid energy by considering demand side management plan and maintenance cost with developed particle swarm algorithm

This paper introduces a pioneering approach to power system optimization by integrating microgrids and demand-side management, with a focus on its novel contributions. Emphasizing cost reduction, the study targets effective demand management through load shifting. A unique microgrid energy management system is proposed for peak-shaving, leveraging the cost advantages of lower controllable unit expenses. The system actively involves microgrid units in responsive load management, reducing peak consumption during high-demand periods. Evaluation of various demand-side management techniques, especially peak-time rebate programs, is conducted through presented indices measuring the energy management solution's effectiveness. To address the complexity, a particle swarm-based algorithm is introduced for computational efficiency. Validation of the proposed method and model in diverse scenarios showcases significant cost reduction, demonstrating the practicality and innovation of the integrated microgrid and demand-side management strategy. The study highlights a substantial reduction in peak load through load response program participation. At a 20 % participation rate with a 10-cent incentive, there's a significant decrease in peak load during specific hours. With increasing participation and incentives, peak load reduction intensifies, reaching optimal results at a 40 % participation rate and a 20-cent incentive.

A swarm-intelligent based load-shifting strategy for clean and economic microgrid operation

The required load in a typical microgrid structure fluctuates from hour to hour. Based on the peaks and troughs of the load demand curve, the power system utilities preset different price of electricity at various periods of the same day which is referred to as time-of-use (TOU) pricing policy. An optimal economic strategy called demand side management (DSM) reduces load during peak hours by shifting the elastic loads and compensates the shifted load by increasing the demand during valley hours. This recreates the total demand pattern on the demand price elasticity relation. The novel benefaction of this research suggests a DSM approach based on a hybrid intelligence technique to attain a compromised solution between minimum generation costs and pollutants emitted by DERs of an LV grid-connected microgrid system. Several grid involvement strategies, power market pricing types, and demand response mechanisms are examined in five separate instances for the microgrid system. The outcomes achieved in each example show that the recommended DSM technique may be applied and is appropriate in terms of cost reduction. When 30–40% of customers participated in the DSM curriculum, the generation cost was reduced by 10–13%. Environment constrained economic dispatch provided a superior compromise amongst lowest generation cost and emission for different microgrid load models. Statistical analysis and comparison of the results from previously published literatures validates the superiority of the proposed approach incorporated with DSM policy.

Non-Intrusive Load Identification of Residential Appliances Using Improved Dictionary Learning Technique

With the advent of time, the demand for power in the residential sector is increasing. Along with supply-side management, the demand side is also used to balance electricity and supply demand. To apply different demand-side management techniques, the energy disaggregation on metered data is used to retrieve information related to available demand. Non-intrusive load monitoring is a technique that separates the total power consumption into appliance loads with minimum invasion of privacy. Non-intrusive load monitoring covers the methods of Stochastic finite state machines, Neural Networks and Sparse Coding. Developing an efficient algorithm for NILM is a key challenge in maximizing energy conservation. Recently, a new deep learning technique called dictionary learning has been developed for energy disaggregation. Smart meters provide the whole house data, and the Dictionary technique is trained to predict an appliance's power or ON/OFF based on its power consumption. This research proposes the event-based dictionary learning technique, which can disaggregate multiple appliances through orthogonal matching pursuit (OMP) and kernel-singular value decomposition (K-SVD). The sparse matrix is predicted through OMP, and K-SVD predicts the dictionary matrix. The training, testing and validation are done on the ECO dataset. The results of this research are noticeable and show the validity of the proposed methodology for energy disaggregation.

Assessment of Applying Demand side management Techniques on distribution networks performance: a practical Case

Assessment of Applying Demand side management Techniques on distribution networks performance: a practical Case

A Systematic Tri-level Demand-driven Supply-side Management Approach on Enhancing Building Energy Performance in an Educational Institution

The concept of demand-side management (DSM) is a ceaseless research topic craving an advanced, superior, and qualitative strategy for improving customers’ level of satisfaction with energy utilization. It encourages the active participation of prosumers and paves the way for sufficient energy proficiency. However, less attention is paid to accounting for consumers’ comfort and priorities. This paper presents a tri-level systematic approach for implementing demand-side and supply-side management at the consumer end. The proposed Tri-level Demand-Driven Supply-Side Management (TDD-SSM) framework commences with gathering knowledge on available loads in the department building block of a higher educational institution situated in the Madurai district of Tamil Nadu, India. The incorporated DSM technique follows strategic load categorization and load scheduling. Following that, a feasibility study on optimal sources is accomplished with Hybrid Optimization of Multiple Energy Resources software. The simulation is done to have a clear comparison in rendering a techno-enviro-economically feasible configuration of energy source to loads (with and without DSM). Ultimately, the framework concludes by picking the most promising energy configuration through the ideal computational multi-criteria decision-making–Preference Ranking Organization Method for Enrichment Evaluation II approach, which highlights the energy configuration of photovoltaic solar generation supply along with grid feeding loads after DSM incorporation and is found to be the best energy configuration under techno-enviro-economic criteria.

Demonstrating clean energy transition scenarios in sector-coupled and renewable-based energy communities

Background Energy communities facilitate several advantages, including energy autonomy, reduced greenhouse gas emissions, poverty mitigation, and regional economic development. They also empower citizens with decision-making and co-ownership prospects in community renewable projects. Integrating renewable energy sources and sector coupling is a crucial strategy for flexible energy systems. However, demonstrating clean energy transition scenarios in these communities presents challenges, including technology integration, flexibility activation, load reduction, grid resilience, and business case development. Methods Based on the system of systems approach, this paper introduces a 4-step funnel approach and a 4-step reverse funnel approach to systematically specify and detail demonstration scenarios for energy community projects. The funnel approach involves four steps. First, it selects demonstration scenarios promoting energy-efficient state-of-the-art renewable technologies and storage systems, flexibility through demand side management techniques, reduced grid dependence, and economic viability. Second, it lists all existing and planned project technologies, analysing energy flows. Third, it plans actions at different levels to implement the demonstration scenarios. Fourth, it validates the strategies using key performance indicators (KPI) to quantify the effectiveness of the planned measures. Furthermore, the reverse funnel approach delves deeper into the demonstration scenarios. The four steps involve identifying stakeholder perspectives, describing scenario scopes, listing conditions for realisation, and outlining business models, including value chains and economic assumptions. Results This approach provides a detailed analysis of the demonstration scenarios, considering actors, objectives, boundary conditions, and business assumptions. The methodologies are exemplified in three diverse European energy communities extending across residential, commercial, tertiary, and industrial establishments, allowing power-to-x and sector coupling opportunities. The paper also suggested thirteen KPIs for validating renewable-focused energy community projects. Conclusions Finally, the paper recommends increased collaboration between energy communities, knowledge sharing, stakeholder engagement, transparent data collection and analysis, continuous feedback, and method improvement to mitigate policy, technology, business, and market uncertainties.

Demand side management using a novel archimedes optimization algorithm in a smart grid environment

Demand side management (DSM) is a smart grid technology that enables consumers to make decisions about their energy use, lowers energy suppliers’ peak hour demand, and changes the load profile. Demand Side Management (DSM) is regarded as the most significant method used in a Smart Grid (SG), as it helps consumers produce accurate information about their electrical energy usage and assists the utility in reducing peak load demand and reshaping the demand curve. By effectively utilising storage with Renewable Energy Systems (RES), DSM seeks to reduce peak demand, electricity costs, and emission rates. In this paper, we have proposed a load-shifting method for the DSM with a large number of controllable devices. The load-shifting issue has been handled hourly, throughout the course of a 24-hour day, in order to reduce the peak demand, lower the power cost, and minimise the Peak to Average load Ratio (PAR). The Archimedes Optimization (AO) method has been utilised in residential loads in SG to achieve the goal of load shifting by minimising of the problem to the DSM. The simulation findings demonstrate that the suggested demand side management technique generates significant cost savings while lowering the smart grid’s peak load demand.

Demand Side Management In Smart Grid Optimization Using Artificial Fish Swarm Algorithm

The demand side management and their response including peak shaving approaches and motivations with shiftable load scheduling strategies advantages are the main focus of this paper. A recent real-time pricing model for regulating energy demand is proposed after a survey of literature-based demand side management techniques. Lack of user’s resources needed to change their energy consumption for the system's overall benefit. The recommended strategy involves modern system identification and administration that would enable user side load control. This might assist in balancing the demand and supply sides more effectively while also lowering peak demand and enhancing system efficiency. The AFSA and BFO algorithms are combined in this study to handle the optimization of difficult problems in a range of industries. Although the BFO will be used to exploit the search space and converge to the optimum solution, the AFSA will be used to explore the search space and retain variation. In terms of reduction of peak demand, energy consumption, and user satisfaction, the AFSA-BFO hybrid algorithm outperforms previous techniques in the field of demand side management in a smart grid context, using an AFSA. According to simulation results, the genetic algorithm successfully reduces PAR and power consumption expenses.

A fair and effective approach to managing distributed energy resources through peer-to-peer energy trading with load prioritization among smart homes

This paper proposes a novel approach to effectively manage distributed energy resources (DERs) by facilitating peer-to-peer (P2P) energy trading among smart homes. This system incentivizes peers to trade surplus energy, thereby maximizing social welfare. The proposed day-ahead demand-side management (DSM) technique considers load prioritization based on individual user needs. It employs an optimal energy allocation policy that encourages peers to purchase energy at reduced rates while selling it at advantageous prices. This strategy optimizes the allocation of traded energy to higher-priority loads, followed by lower-priority ones, all while considering user preferences. The proposed method results in an average reduction in energy costs from 14.80 % to 2.70 % for all consumers and an average increase in revenues from 17.35 % to 9.07 % for all prosumers compared to the grid and previously proposed studies. Moreover, the proposed approach significantly enhances the consumer satisfaction Index (SI), with a 46.8 % increase over the previous research study. Simulation results demonstrate substantial economic benefits for all market participants, improved SI, enhanced community self-sufficiency, and reduced dependence on the main grid. The proposed methodology offers a practical and fair solution for managing DERs through P2P energy trading by implementing load prioritization within smart homes. It highlights the potential to establish a sustainable and efficient energy ecosystem, thereby enhancing the effective utilization of surplus energy from DERs.

Linear regression approach for performance evaluation of ES with load impedance variations of non-critical and critical load

Due to the strengthening of decentralized power generation technology, renewable energy sources (RES) are developing moderately as next-generation additions to the modern power system. However, dense penetration of RES disturbs the stability of voltage and frequency. The Electric spring (ES) is a new advanced device that deals with voltage fluctuations due to the intermittent nature of RESs in distributed networks. Electric spring is an emerging demand-side management technique in the smart grid that can support the weakly regulated system voltage. This paper investigates the effect of load impedance variation of non-critical load and critical load on the performance of ES through MATLAB simulations. The loads considered are linear. The results obtained from the simulation study are used to construct a mathematical model using a simple linear regression technique. The results from the simulation study and linear regression analysis are similar. It is observed that the linear regression approach outperformed the simulation approach in terms of time complexity and predicted the real and reactive power management provided by ES for under-voltage and over-voltage conditions.

Demand-Side and Utility-Side Management Techniques for Increasing EV Charging Load

Electricity authorities need capacity assessment and expansion plans for efficiently charging the growing Electric vehicle (EV) fleet. Specifically, the distribution grid needs significant capacity expansion as it faces the most impact to accommodate the high variant residential EV charging load. Utility companies employ different scheduling policies for the maintenance of their distribution transformers (hereinafter, XFR). However, they lack scenario-based plans to cope with the varying EV penetration across locations and time. The contributions of this paper are twofold. First, we propose a customer feedback-based EV charging scheduling to simultaneously minimize the peak load for the distribution XFR and satisfy the customer needs. Second, we present a deep reinforcement learning (DRL) method for XFR maintenance, which focuses on the XFR’s effective age and loading to periodically choose the best candidate XFR for replacement. Our case study for a distribution feeder shows the adaptability and success of our EV load scheduling method in reducing the peak demand to extend the XFR life. Furthermore, our DRL-based XFR replacement policy outperforms the existing rule-based policies. Together, the two approaches provide a complete capacity planning tool for efficient XFR maintenance to cope with the increasing EV charging load.

A novel energy storage and demand side management for entire green smart grid system for NEOM city in Saudi Arabia

AbstractHybrid renewable energy systems (HRES) are gaining high interest in supplying electric energy for remote communities. Energy storage systems (ESS) are utilized by green autonomous HRESs to accommodate the variability of renewable resources such as wind and solar energy systems. The lack of any traditional energy source is adding a great reliability challenge which should be compensated using expensive ESS. This challenge can be avoided by using a pumped hydro energy storage system (PHES) in harmony with batteries. The PHES is an excellent option to be used in NEOM city due to the perfect topographical characteristic of this site. The minimum cost of energy and the highest reliability is used as an objective for sizing the proposed entire green HRES. Using smart grid principles (SGP) and demand‐side management (DSM) in the design and operation stages will minimize system size and cost, which can result in a significant reduction in consumer bills. As a result, this paper introduces an innovative DSM based on a dynamic tariff. The suggested DSM technique was developed utilizing a unique fuzzy logic that takes into account the present and day‐ahead ESS situations to intelligently determine the ideal tariff for the lowest cost and maximum reliability of the HRES. This paper introduces a modified grey wolf optimization (MGWO) technique to shorten convergence time while preserving the best accuracy. The suggested MGWO is assessed against 10 swarm optimization techniques. The payback period of the project is 7 years. The findings acquired from this unique program demonstrated its superiority, with conversion times reduced by 22% to 80% when compared to previous optimization procedures. Furthermore, as compared to the flat rate pricing tariff, the usage of the dynamic tariff lowered the LCOE by 53%.

Using Energy Conservation-Based Demand-Side Management to Optimize an Off-Grid Integrated Renewable Energy System Using Different Battery Technologies

Rural electrification is necessary for both the country’s development and the well-being of the villagers. The current study investigates the feasibility of providing electricity to off-grid villages in the Indian state of Odisha by utilizing renewable energy resources that are currently available in the study area. However, due to the intermittent nature of renewable energy sources, it is highly improbable to ensure a continuous electricity supply to the off-grid areas. To ensure a reliable electricity supply to the off-grid areas, three battery technologies have been incorporated to find the most suitable battery system for the study area. In addition, we evaluated various demand side management (DSM) techniques and assessed which would be the most suitable for our study area. To assess the efficiency of the off-grid system, we applied different metaheuristic algorithms, and the results showed great promise. Based on our findings, it is clear that energy-conservation-based DSM is the ideal option for the study area. From all the algorithms tested, the salp swarm algorithm demonstrated the best performance for the current study.

Energy Internet-Based Load Shifting in Smart Microgrids: An Experimental Study

This study investigated a grid-connected smart microgrid (MG) system integrating solar photovoltaic (PV) panels and a battery energy storage system (BESS) as distributed energy resources (DERs) to locally serve residential loads. The load-shifting demand-side management (DSM) technique was employed to effectively manage the load appliances. The proposed load-shifting algorithm relies on minimum price incentives to allow customers to allocate their load appliances economically during minimum price periods. The algorithm considers the waiting times and minimum tariff periods for appliances, calculates precise operating durations for each appliance, and prioritizes powering the appliances from the MG first, followed by the main grid. The system comprises two non-shiftable and three shiftable loads. When the MG power is insufficient to activate all shiftable loads, the system transfers the remaining unsupplied shiftable appliances to periods with low-priced energy. The Energy Internet concept is adopted to manage energy and monitor usage when a customer is unable to check the accuracy of their energy meter by supervising the system’s features on-site. The proposed comprehensive system enables load management, continuous monitoring, customer awareness, and energy cost saving. Six cases were studied, both numerically and experimentally, with varying MG power generation and load pre-scheduling periods, with and without DSM application. In all adopted cases, the implemented system save energy costs by at least 50%.

Survey of Electricity Demand Forecasting and Demand Side Management Techniques in Different Sectors to Identify Scope for Improvement

Electricity demand is increasing at a rapid rate. Sustainability related challenges are posing an immediate cause of concern for the planet. Smart Grid provides an efficient way to manage the complex scenario. The challenge of enhancing energy efficiency and integration of renewables effectively is addressed by utilizing smart grid technology. Accurate demand forecasting and demand side management are needed to balance the demand and supply gaps over time of day and season related variations. This survey paper aims to provide a comprehensive overview of the various techniques and approaches used for demand forecasting in the residential, industrial, and agricultural sectors. It also examines the role of demand response in managing peak electricity demand and maintaining grid stability through the shifting of usage to off-peak hours. The aim is to understand the nuances and prevailing practices in these sectors to identify potential areas for improvement.

Novel Architecture for Transactive Energy Management Systems with Various Market Clearing Strategies

The recent advancements in demand-side management techniques add significant benefits to the distribution systems. One such technique is transactive energy management systems (TEMS) which motivate the energy end-users to take part in local energy trading. The end-users can effectively increase the monetary benefits by trading the surplus generation/demand within the local energy market (LEM). The LEM operator frames a viable market clearing strategy to fix the market clearing price to enhance the monetary benefits of all the market players. In this study, LEM architecture with different market clearing strategies is proposed for TEMS to ensure profitable power transactions between the neighboring end-users. An optimal energy management algorithm is also proposed for time scheduling the operation of flexible loads and batteries, considering dynamics in end-users’ behavior, variations in utility parameters, and the intermittent nature of renewable power generation. Further, an optimal load scheduling algorithm is developed at the end-users’ premises to improve the profits in the LEM. Correspondingly, the trading strategies are extended to increase market reliability by penalizing participants for their abnormal activities in energy trading. The proposed framework is validated with different case studies considering ten residential participants in a locality.

Development of fuzzy logic-based demand-side energy management system for hybrid energy sources

Demand-side energy management techniques, such as load shielding, shifting, and delaying appliance operation during peak periods, are typically used to reduce electricity costs at the expense of users’ comfort. To address these challenges, especially where operational delays are unacceptable, distributed generation (DG) is frequently incorporated into the grid system to improve power balance and total energy costs. However, dynamic load demands and varying outputs from renewable DG sources such as solar photovoltaic (PV) systems make energy management in microgrids (MGs) extremely challenging. Moreover, most of the existing studies in this domain focus on objective functions that are geared on optimising the economic balance between cost and value of MG operation over a certain time period. Nevertheless, research that took into consideration the stochastic behaviour of DG’s subsystems in addition to cost and benefit of MG operation are still limited. This current study proposed a fuzzy logic control (FLC) integrated energy management system (EMS) for commercial loads with hybrid grid-solar PV/battery energy system. The proposed technique intelligently selects energy sources using the grid energy cost and the state of charge (SoC) of the solar PV/battery at any time of the day. The EMS operate the loads at a reduced cost without any operational delay or shifting. The system was implemented in the MATLAB/Simulink environment, and the techno-economic feasibility of energy cost savings was investigated by comparing the developed scheme with the Homer hybrid energy system model for a hotel building. The developed EMS reduced energy costs by an average of 11.87 % per day and 7.94 % over a 20-year lifetime.