Demand Side Management Techniques Research Articles

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.

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.

RECLAIM: Renewable Energy Based Demand-Side Management Using Machine Learning Models

The diesel generators sets (DGs) and battery storage systems (BSS) are the essential energy sources in a modern high-rise buildings. In this paper DG, BSS and Photovoltaic system (PV) has been considered to minimize the grid power injection using a centralized Energy Management System (EMS). Machine Learning (ML) techniques are used to predict the performance of various regression models by comparing grid power and load curves. It includes Artificial Neural Network (ANN), Wide Neural Network (WNN), Linear Regression (LR), Linear Regression Interaction (LR-I), Linear Regression Stepwise (LR-S), Regression Fine Tree (RF-T), Regression Coarse Tree (RC-T) and Gaussian Process Regression (GPR) based techniques. The Demand Side Management (DSM) techniques such as peak shaving and valley filling is integrated with ML technique in a Hybrid energy source (HS) system.The comparative analysis of results depicts the effective reshaping of the grid profile without scheduling or disconnecting the loads. Matlab simulation software is used to validate the results.

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.

Review on optimization techniques and role of Artificial Intelligence in home energy management systems

Present advancements in the power systems paved way for introducing the smart grid (SG). A smart grid is beneficial to consumers which enables the bi-directional flow of information between the utility and customer. Demand-side management (DSM) techniques are crucial as load-side management techniques to attain the better stability of the grid. Home energy management systems (HEMS) play a indispensable part in the DSM. Countless traditional optimization techniques are utilized to implement HEMS, but the limitations of traditional Math heuristic methods gave rise to a concept-based optimization techniques called the Meta heuristic methods. Recent advancements introduced smart optimization techniques powered by Artificial Intelligence (AI). This article elucidates the applications of AI-based optimization techniques and their advantages over other methods. Various Machine learning (ML) and Deep Learning (DL) algorithms and their utilization for HEMS are discussed in brief.

A Demand Side Management Control Strategy Using RUNge Kutta Optimizer (RUN)

Demand side management initiatives have gained attention recently because of the development of the smart grid and consumer-focused regulations. The demand side management programme has numerous goals. One of the main goals is to control energy demand by altering customer demand. This can be done in several ways, including financial discounts and behaviour changes brought about by providing knowledge to support the grid’s stressed conditions. In this study, demand side management techniques for future smart grids are presented, including load shifting and strategic conservation. There are many controlled devices on the grid. The load shifting and day before strategic conservation approaches mentioned in this study are derived analytically for the minimization problem. For resolving this minimization issue, the RUNge Kutta optimizer (RUN) was developed. On a test smart grid with two service zones, one with residential consumers and the other with commercial consumers, simulations are performed. By contrasting the outcomes with the slime mould algorithm (SMA), Sine Cosine Algorithm (SCA), moth–flame optimization (MFO), and whale optimization algorithm (WOA), RUN demonstrates its effectiveness. The simulation findings demonstrate that the suggested demand side management solutions produce significant cost savings while lowering the smart grid’s peak load demand.

Demand side management of electric vehicles in smart grids: A survey on strategies, challenges, modeling, and optimization

The shift of transportation technology from internal combustion engine (ICE) based vehicles to electric vehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency has brought EV technology to the forefront of the electric power distribution systems due to their ability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoption of EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillary service-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenario by incorporating demand-side management (DSM) through EV integration. The integration of EVs with DSM techniques is hurdled with various issues and challenges addressed throughout this literature review. The various research conducted on EV-DSM programs has been surveyed. This review article focuses on the issues, solutions, and challenges, with suggestions on modeling the charging infrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separately to enhance the EV-DSM operation. Gaps in current research and possible research directions have been discussed extensively to present a comprehensive insight into the current status of DSM programs employed with EV integration. This extensive review of EV-DSM will facilitate all the researchers to initiate research for superior and efficient energy management and EV scheduling strategies and mitigate the issues faced by system uncertainty modeling, variations, and constraints.

Hydrogen Production from Surplus Electricity Generated by an Autonomous Renewable System: Scenario 2040 on Grand Canary Island, Spain

The electrification of final energy uses is a key strategy to reach the desired scenario with zero greenhouse gas emissions. Many of them can be electrified with more or less difficulty, but there is a part that is difficult to electrify at a competitive cost: heavy road transport, maritime and air transport, and some industrial processes are some examples. For this reason, the possibility of using other energy vectors rather than electricity should be explored. Hydrogen can be considered a real alternative, especially considering that this transition should not be carried out immediately because, initially, the electrification would be carried out in those energy uses that are considered most feasible for this conversion. The Canary Islands’ government is making considerable efforts to promote a carbon-free energy mix, starting with renewable energy for electricity generation. Still, in the early–mid 2030s, it will be necessary to substitute heavy transport fossil fuel. For this purpose, HOMER software was used to analyze the feasibility of hydrogen production using surplus electricity produced by the future electricity system. The results of previous research on the optimal generation MIX for Grand Canary Island, based exclusively on renewable sources, were used. This previous research considers three possible scenarios where electricity surplus is in the range of 2.3–4.9 TWh/year. Several optimized scenarios using demand-side management techniques were also studied. Therefore, based on the electricity surpluses of these scenarios, the optimization of hydrogen production and storage systems was carried out, always covering at least the final hydrogen demand of the island. As a result, it is concluded that it would be possible to produce 3.5 × 104 to 7.68 × 104 t of H2/year. In these scenarios, 3.15 × 105 to 6.91 × 105 t of water per year would be required, and there could be a potential production of 2.8 × 105 to 6.14 × 105 t of O2 per year.

A comprehensive review on energy management, demand response, and coordination schemes utilization in multi-microgrids network

The multi-microgrids (MMGs) concept has recently got more attention due to its features of accommodating large-scale integration of renewable generation with efficient utilization, improved power system efficiency, reliability and stability performance through coordination and cooperation of energy exchange among the microgrids (MGs) and the main grid. Moreover, utilization of demand response programs in MMG structure can also bring wide range of potential economic benefits to system operation and stability. However, various factors including electrical load variation, the unpredictable nature of renewable energy resources, the coordinated energy trading among microgrids and cyber-attacks on communication based coordinated control can pose great challenge to achieve stable, effective, secure and optimal operation of power system. In this paper, we provide a comprehensive review on the contemporary multi-microgrid architectures, energy management functionalities and multi-objectives energy management schemes in MMGs. Also, an overview of demand side management techniques and coordination schemes incorporating energy transaction and resilience operation in MMGs is provided. The importance of cyber-security in MMGs is highlighted and different attacks are reported. Finally, future recommendations on contemporary literature are provided.

Home Energy Recommendation System (HERS): A Deep Reinforcement Learning Method Based on Residents’ Feedback and Activity

Smart home appliances can take command and act intelligently, making them suitable for implementing optimization techniques. Artificial intelligence (AI) based control of these smart devices enables demand-side management (DSM) of electricity consumption. By integrating human feedback and activity in the decision process, this work proposes a deep Reinforcement Learning (RL) method for managing smart devices to optimize electricity cost and comfort residents. Our contributions are twofold. Firstly, we incorporate human feedback in the objective function of our DSM technique that we name Home Energy Recommendation System (HERS). Secondly, we include human activity data in the RL state definition to enhance the energy optimization performance. We perform comprehensive experimental analyses to compare the proposed deep RL approach with existing approaches that lack the aforementioned critical decision-making features. The proposed model is robust to varying resident activities and preferences and applicable to a broad spectrum of homes with different resident profiles.

Platform for transverse evaluation of control strategies for multi-energy smart grids

This paper presents the PEACEFULNESS software platform (Platform for transvErse evAluation of Control stratEgies For mULti-eNErgy Smart gridS), an open framework dedicated to multi-energy smart-grids, based on a techno-economic model that integrates economic considerations (contracts). As such, it is mainly oriented towards the evaluation of multi-energy grid supervision strategies, that is, energy management, and the corresponding policies and legal organization. The main goal is then to highlight the various possible behaviors and strategies to organize the probable future interconnections between the different energy carriers. In particular, it aims at investigating how to maximize the use of renewable energy sources (RES), using Demand Side Management (DSM) techniques and energy storage, in a shared economy context. The open-source tool PEACEFULNESS, written in Python, is described here in detail. It combines a top-down description of the energy networks and connections between the various agents (energy providers, distribution system operators, aggregators, consumers, producers, prosumers, etc.), together with a techno-economic bottom-up description for all devices. Here, both public databases and users’ data (basic heating demands or based on building modeling) can be used, as well as generic or more specific models (e.g., PV panels with constant or temperature-dependent efficiency). One of its major unique features compared with other tools is that it extends the use of DSM techniques to various energy grids which can also interact together. Furthermore, different economic models can be set for both the aggregators and the customers, and even within these groups. As a last competitive advantage, PEACEFULNESS allows the user to simulate the operation and supervision of tens up to hundreds of thousands of agents. It also provides a reporting system giving access to all the data, with a configurable granularity and frequency for the retained indicators. Finally, several validation cases are presented, followed by a series of test cases with increasing size: a smart home, a smart district (2 000 dwellings) and a smart community (50 000 dwellings).

A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes

The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak load, maximize local energy generation and improve consumer comfort. To increase the effectiveness of demand response programs for smart homes, various demand-side management strategies are used to enable consumers to optimally manage their loads. This study lists out DSM techniques used in the literature for appliance scheduling. Most of these techniques aim at energy management in residential sectors to encourage users to schedule their power consumption in an effective manner. However, the performance of these techniques is rarely analyzed. Additionally, various factors, such as consumer comfort and dynamic pricing constraints, need to be incorporated. This work surveys most recent literature on residential household energy management, especially holistic solutions, and proposes new viewpoints on residential appliance scheduling in smart homes. The paper concludes with key observations and future research directions.

Home Energy Management Systems: Operation and Resilience of Heuristics Against Cyberattacks

Internet of Things (IoT) and advanced communication technologies have demonstrated great potential to manage residential energy resources by enabling demand-side management (DSM). Home energy management systems (HEMSs) can automatically control electricity production and usage inside homes using DSM techniques. These HEMSs wirelessly collect information from hardware installed in the power system and homes with the objective of intelligently and efficiently optimizing electricity usage and minimizing costs.

Demand Side Management Using Battery Energy Storage System for a Sample Residential Load of Pakistan

This research is a step towards the sustainable energy development through the mitigation of the electrical energy crisis using the batteries for not only solving the intermittence but also for demand side management., mostly faced when the energy demand reaches the peak value. An algorithm is presented for the optimal usage of energy by demand side management (DSM) through peak shaving using battery energy storage system (BESS) in the residential areas for developing countries like Pakistan. The data set used is the PRECON data set based on the original demand data collected of 42 houses of Lahore, Pakistan for years 2018-2019. The load profiles for the individual and group of houses are used and analyzed. Daily, monthly and annual load factors are calculated for the groups of houses based on the stochastic data based on realistic data. Daily load factors are improved through demand side management technique such as peak shaving at individual and municipal level. Loads are categorized from the given load profiles into base load, peak load and mid-range load. The cost advantage related to the type of the battery is also considered. Research concludes to the advantages of peak shaving, load factor improvement, load profile improvement and possibility of providing optimal and reliable energy with reasonable energy prices.

Load shifting impact on generating adequacy assessment during peak period

<p><span>The load shifting technique is widely implemented in electrical power generation due to its considerable impact on system reliability. The evaluation of load shifting benefits towards the adequacy of generation systems requires an accurate assessment. If the generation unit’s capacity is insufficient to meet the system load, then assistance is required from alternative sources. Load shifting, as a primary demand-side management technique, is used efficiently in electrical power networks given that the energy clipped/curtailed owing to load curtailment and peak clipping can be recovered during the off-peak period. The reliability of a generic framework for the prospective integration of a load shifting technique, with preventative and corrective actions as alternatives to peaking units, is investigated in this study. The optimal rate of load shifting in terms of expected energy not supplied is also investigated. Results show that preventive load shifting (PLS) can act as peaking units when the total generated capacity is within specific limits. Meanwhile, corrective load shifting can act as a better alternative than PLS and peaking units. To calculate expected energy not supplied, sequential Monte Carlo simulation is utilized. This study is conducted using the IEEE reliability test system.</span></p>

Soft Load Shedding Based Demand Control of Residential Consumers

Power generation and consumption is an instantaneous process and maintaining the balance between demand and supply is crucial since the demand and supply mismatch leads to various risks like over-investment, over-generation, under-generation, and the collapse of the power system. Therefore, the reduction in demand and supply mismatch is critical to ensure the safety and reliability of power system operation and economics. A typical and common approach, called full load shedding (FLS), is practiced in cases where electric power demand exceeds the available generation. FLS operation alleviates the power demand by cutting down the load for an entire area or region, which results in several challenges and problems for the utilities and consumers. In this study, a demand-side management (DSM) technique, called Soft-load shedding (SLS), is proposed, which uses data analytics and software-based architecture, and utilizes the real-world time-series energy consumption data available at one-minute granularity for a diversified group of residential consumers. The procedure is based on pattern identification extracted from the dataset and allocates a certain quota of power to be distributed on selected consumers such that the excessive demand is reduced, thereby minimizing the demand and supply mismatch. The results show that the proposed strategy obtains a significant reduction in the demand and supply mismatch such that the mismatch remains in the range of 10–15%, especially during the period where demand exceeds generation, operating within the utility constraints, and under the available generation, to avoid power system failure without affecting any lifeline consumer, with a minimum impact on the consumer’s comfort.

Various demand side management techniques and its role in smart grid–the state of art

The current lifestyle of humanity relies heavily on energy consumption, thus rendering it an inevitable need. An ever-increasing demand for energy has resulted from the increasing population. Most of this demand is met by the traditional sources that continuously deplete and raise significant environmental issues. The existing power structure of developing nations is aging, unstable, and unfeasible, further prolonging the problem. The existing electricity grid is unstable, vulnerable to blackouts and disruption, has high transmission losses, low quality of power, insufficient electricity supply, and discourages distributed energy sources from being incorporated. Mitigating these problems requires a complete redesign of the system of power distribution. The modernization of the electric grid, i.e., the smart grid, is an emerging combination of different technologies designed to bring about the electrical power grid that is changing dramatically. Demand side management (DSM) allow customers to be more involved in contributors to the power systems to achieve system goals by scheduling their shiftable load. Effective DSM systems require the participation of customers in the system that can be done in a fair system. This paper focuses primarily on techniques of DSM and demand responses (DR), including scheduling approaches and strategies for optimal savings.

AI Powered IoT based Home Energy Management System towards DG Integration

This paper attempts to present an Internet of Things (IoT) based Home Energy Management System (HEMS) to accommodate Artificial Intelligence (AI) based Distributed Generation (DG) Integration in Smart Micro Grid environment. This work presents the feasibility of load and weather Big Data acquisition, online load forecasting /prediction, online load priority assignment, online peak clipping estimation on the cloud (Thingspeak). Load priority and peak clipping Demand Side Management (DSM) Techniques are used to run Load Management Algorithm (LMA) on the cloud which ultimately executes AI-based DG Integration at the load center. Based on the real time load and weather Big Data Load Priority Table (LPT) is generated on the cloud in order to assign the priority to the loads of the load center and online forecasting is also carried out on the cloud to estimate Total Power PT using Artificial Neural Network (ANN) and five different ML algorithms which are later utilized to estimate Threshold /Peak Clipping Power PTh. Using these LPT and PTh values LMA executes AI-based DG Integration. To that end, a novel IoT-based HEMS is designed and fabricated to run cloud-based LMA in order to accommodate proposed DG Integration. Amongst all the prediction algorithms trained on the cloud to estimate PTh, Linear Regression (LR) ML algorithm is witnessed to have lowest RMSE of 4.44e−13, MSE of 1.97e−25 and MAE of 2.11e−13. MATLAB 2018b licensed version is used to run ANN and ML algorithms based forecasting/prediction on the cloud.

Simulation Studies to Quantify the Impact of Demand Side Management on Environmental Footprint

The increased use of energy leads to increased energy-related emissions. Demand side management (DSM) is a potential means of mitigating these emissions from electric utility generating units. DSM can significantly reduce emissions and provide economic and reliability benefits. This work presents some DSM techniques, such as load shifting, energy conservation, and valley filling. Furthermore, this work explains the most common DSM programs. To quantify the effect of DSM in diminishing carbon footprint, this paper performs power flow analysis on a yearly load profile corresponding to Fort Collins, Colorado, U.S. This work used the IEEE 13-node test system to simulate several scenarios from the multi-criteria decision-making (MCDM) alternatives, both individually and integrated. For the base case, emissions decrease by 16% from the 2005 level. The “energy conservation” option achieved a 20% reduction in emissions, integrating both alternatives increased the emissions mitigation up to 22%. Simulation of the residential sector shows the “communication and intelligence” option reduces emissions about 14% from the 2005 level. A scenario that combines “electric stationary storage” with “communication and intelligence” diminishes the emissions by more than 15%. The last scenario examined all MCDM alternatives combined into one option, resulting in a 20% emissions reduction. We also conducted a cost benefit analysis (CBA) to investigate economic, technical, and environmental costs and benefits associated with each alternative. The economic evaluation shows that “electric stationary storage” is the best option since it charges during lower electricity prices and discharges during peaking demand. The economic analysis presents a trade-off chart, so the decision maker can select the alternative based on their preference.