Demand Side Management Algorithm Research Articles

Demand side management using optimization strategies for efficient electric vehicle load management in modern power grids.

The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.

Intelligent categorization and interactive mechanism for smart demand side management of residential consumers

Power Grids are becoming resilient to the erupting complications of imbalanced supply and demand through intelligent technologies that analyze and allow communication between utility and consumers. This paper proposes a strategy for smart energy management among residential consumers using machine learning algorithm that intelligently classifies high power consumers contributing to peak demand in every quarter of a day as Category A, Category B, Category C and Category D. Classification is performed on daily load profile data of 29,546 residential consumers using different supervised learning techniques. The raw data of consumers are preprocessed with statistical analysis to label peak consumers in different time slots and highest testing accuracy of 92.23% has been achieved with Random Forest classifier. The peak loads of classified consumers are then individually clipped to curtail the load of every consumer within baseline power of every quarter. Adoption of this methodology will facilitate to provide recommendations to every classified consumer of different categories for load curtailment and imposition of penalty based on the baseline limit. The power deficits during different quarters of a day estimated as 0.968%, 0.740%, 0.993% and 0.164% of load have been effectively adjusted by the classified consumers with per capita load adjustment of 3.641 kW, 1.164 kW, 1.628 kW and 0.984 kW respectively. Thus, overall power deficit, 0.73% of daily total load (12,265.7 kW) has been mitigated through intelligent distribution of load with minimal stress on consumers, which proves the efficiency of proposed Demand Side Management algorithm in sustaining growing demands within limited resources.

Energy, economics and environmental (3E's) analysis of a solar-assisted HRES through demand side management.

The limitation of renewable energy is its fluctuation with time, season, and peak load demand. Hence, most of the research was carried using demand side management to reduce the actual consumption load to lower the peak load. This will also hamper the basic energy need of the facility. Therefore, the objective of the present study is to model a hybrid renewable energy system (HRES) to attain the specific consumption of a community health centre (CHC) in Yomcha, Arunachal Pradesh, with load shedding. The optimization of HRES is performed on HOMER® Pro software along with the demand side management algorithm and a maximum annual capacity shortage of 5%. Three different cases were compared based on feasibility, economics, and environmental basis to estimate the performance of the HRES. The results show that the proposed model is appropriate for the CHC over the previously proposed system. Moreover, HRES with (design-side management) DSM will reduce the overall cost and increase efficiency and reliability with an net present value of Rs. 3013482 and a cost of energy of Rs. 5.42/kW, renewable fraction 78%, internal rate of return 70%, return of investment 66%, and payback period of 1.4 years. Moreover, it reduces emissions, controls overloading during peak hours, and decreases load shifting. The study also supports the sustainable development goal (SDG)-7 set by the United Nation SDG Committee.

Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles

With the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the efficient operation and management of these systems challenging. Objective of this study is to propose a real-time management system for EV charging, which maximises the renewable energy utilization. An electric power distribution network with an average and peak demands of 1.51 MW, and 3.6 MW respectively, was chosen for the study. The real time power flow through the network components were analyzed using the OpenDSS model. With a wind power density of 574.51 W/m2 and a solar insolation of 4.14 kWh/m2/day, an optimized renewable energy system consisting of a 2.3 MW wind turbine and 2.61 MWp photovoltaic power plant are proposed for the network. Models based on k-Nearest Neighbors algorithms were developed for predicting the performances of these renewable energy systems at the network area. Based on the load profile, power flow analysis, and the predicted generation from solar and wind systems, a demand side management algorithm has been developed for the charge/discharge scheduling of the electric vehicles connected within the network. The basic objective of the algorithm is to maximize the renewable energy utilization by triggering the charging cycle during the periods of excess renewable energy generation. With an annual contribution of renewables is estimated as 12.61 GWh out of which 9.33 GWh is from wind and 3.29 GWh from solar. Wind from wind and solar energy systems, the proposed scheduling algorithm could contribute 71.56 percent of the charging load demand by the EVs.

AHHO: Arithmetic Harris Hawks Optimization algorithm for demand side management in smart grids

Energy management strategies are crucial to efficiently scheduling appliances and preventing peak generation due to increased energy demand. It is essential to manage the demand and supply of energy based on the consumer’s consumption patterns using various heuristic optimization techniques. Additionally, the end-user is more concerned with minimizing electricity costs and reducing peak-to-average ratios (PARs). This work proposes an arithmetic Harris hawks optimization (AHHO) as a new approach for improving the Harris hawks algorithm to optimize residential demand response (DR) load management in a smart grid. Our method employs arithmetic and lightweight flight operators based on the Lévy flight distribution to generate diverse design solutions and improve the HHO’s exploration capabilities. We consider 15 smart appliances and categorize them based on how much energy they use, computing the electricity price using real-time pricing (RTP) and critical peak pricing (CPP). While maintaining user satisfaction within operational and power limits, the objective is to decrease energy costs and PAR. We evaluate the effectiveness of our proposed AHHO approach against nine cutting-edge algorithms using both RTP and CPP schemes. The findings demonstrate that our suggested approach performs better than the other algorithms because it achieved cost savings of 42.10% and 30% under RTP and CPP schemes, respectively. Meanwhile, it also reduced PAR by 55.17% and 50% under RTP and CPP schemes, respectively.

Improving convergence properties of autonomous demand side management algorithms

For implementing Autonomous Demand Side Management (ADSM) in the smart grid, when there is a large number of consumers, there are some problems like low convergence speed and large volume of calculation for finding the optimal amount of energy consumption in any time slot. In this paper, we propose a new method to speed up the convergence of ADSM algorithms and to reduce the volume of computations. When the consumers try to calculate their energy consumption individually in parallel, a problem of peak shifting will be appeared, which causes divergence or slow convergence rate in the ADSM algorithm. Therefore, we modify the ADSM algorithm such that the consumers can avoid peak shifting while making decisions in parallel. The proposed algorithm increases the convergence rate via solving a primal problem instead of the main problem, and decomposing it for each consumer to solve it in parallel. The proposed algorithm is compared with the ADSM algorithms suggested for the same system model in the literature. Our results show a significant improvement in convergence rate and computation volume. In summary, the proposed algorithm is more than 7.5 times faster than the existing algorithms and reduces the number of required operations by more than 2.8 times.

Optimal power tracking for autonomous demand side management of electric vehicles

Increasing electric vehicle penetration leads to undesirable peaks in power if no proper coordination in charging is implemented. We tested the feasibility of electric vehicles acting as flexible demands responding to power signals to minimize the system peaks. The proposed hierarchical autonomous demand side management algorithm is formulated as an optimal power tracking problem. The distribution grid operator determines a power signal for filling the valleys in the non-electric vehicle load profile using the electric vehicle demand flexibility and sends it to all electric vehicle controllers. After receiving the control signal, each electric vehicle controller re-scales it to the expected individual electric vehicle energy demand and determines the optimal charging schedule to track the re-scaled signal. No information concerning the electric vehicles are reported back to the utility, hence the approach can be implemented using unidirectional communication with reduced infrastructural requirements. The achieved results show that the optimal power tracking approach has the potential to eliminate additional peak demands induced by electric vehicle charging and performs comparably to its central implementation. The reduced complexity and computational overhead permits also convenient deployment in practice.

A High-Gain Non-Isolated Three-Port Converter for Building-Integrated PV Systems

Using a hybrid renewable energy source with an energy storage system, this paper proposed a novel multi-stage non-isolated three-port converter with a 5H inverter to feed a residential load varying from 50 Watts to 3500 Watts. The proposed three-port converter operates in grid-tied and standalone power modes. A novel demand-side management algorithm, which covers eight operation modes, is described. Additionally, a complete control system is discussed. The proposed control system controls the PV maximum power point and battery charging/discharging, and regulates the 400 V DC bus voltage, the load voltage in standalone mode, and the grid-tied injected current. The proposed converter and the control system are validated through simulation for all power modes. The simulation results reveal that the proposed system is viable for Building-Integrated PV Systems.

Design and Implementation of Multi-Source and Multi-Consumer Energy Sharing System in Collaborative Smart Microgrid Installation

Many published studies debated electrical energy management. They mainly investigate the multi-source installation to develop energy efficiency during its different phases: production, distribution, and consumption. Although it is rarely discussed, energy sharing is a critical part of the energy management system. In this contribution, a demand-side management algorithm is developed, that incorporates energy consumption scheduler capacity. It provides optimal energy sharing, counting on suitable energy cost parameters and adequate multi-source installation. Using this proposal, the electrical bill decreases thanks to the optimal daily attribution of schedules to households formed by a multi-consumer microgrid. This application guarantees a maximal reduction of electrical cost for the set of energy partners as one prosumer used to consume and produce power. In addition, it maintains energy efficiency as it aids in avoiding breakdowns, and depressing the peak-to-average ratio. It admits that the utility company is, as usual, always reachable non-renewable source. At the same time, renewable energy was engendered by photovoltaic panels concomitant with wind turbines stations. The application is based on the JNET protocol stack. The proposed energy sharing algorithm is implemented by using Arduino board and JN5148 nodes as a star Wireless Sensors Network topology. It is installed as a prototype in the Digital Research Center of Sfax in Tunisia. This proposed incentive-based algorithm managed to reduce the smart microgrid annual cost by almost 55% without harming the public utility. It can even ensure a more significant diminution by selling the surplus of renewable power at the end of each day.

A hybrid genetic algorithm (GA)–particle swarm optimization (PSO) algorithm for demand side management in smart grid considering wind power for cost optimization

A hybrid genetic algorithm (GA)–particle swarm optimization (PSO) algorithm for demand side management in smart grid considering wind power for cost optimization

Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems

In this new era of high electrical energy dependency, electrical energy must be abundant and reliable, thus smart grids are conducted to deliver load demands. Hence, smart grids are implemented alongside distributed generation of renewable energies to increase the reliability and controllability of the grid, but, with the very volatile nature of the Distributed Generation (DG), Demand Side Management (DSM) helps monitor and control the load shape of the consumed power. The interaction of DSM with the grid provides a wide range of mutual benefits to the user, the utility and the market. DSM methodologies such as Conservation Voltage Reduction (CVR) and Direct Load Control (DLC) collaborate in the reduction of plant generation and reciprocally to the comprehensive cost. The aim of this paper is to investigate the effects caused by the implementation of DSM on the operation of PV-integrated distribution systems. The algorithms of CVR, DLC and the combination of CVR and DLC were implemented using OpenDSS and MATLAB. The effectiveness of the aforementioned schemes was verified on IEEE 30-Bus test system. Various possible integration scenarios between Photovoltaic (PV) and DSM schemes are illustrated. The optimal integration of such schemes constraining the reduction of energy consumed by the user and utility is presented. The results show that the implemented DSM algorithms provide a noticeable reduction in energy losses and reduction in consumed energy.

Role of Demand Side Management in Residential Distribution Systems with the Integration of Electric Vehicles

Electric vehicles (EVs) are being well known because of no emissions and eco-friendly and consequently these are extremely preferred for transportation. Conversely, the increasing demand for EVs results in several challenges in the maintenance of the distribution system. In general, EVs are often charged in residential areas. Due to this uncoordinated EV charging the low voltage (LV) distribution network has to encounter many challenges to sustain for the new load conditions without any power outages and voltage fluctuations. If this impact of EVs on the distribution network is not measured and rectified properly, it leads to the replacement and reinforcement of the distribution system which is pronounced and tedious. This work proposes Demand Side Management (DSM) technique to decrease the hassle in the residential distribution system embedded with EVs. DSM is the best alternative technique for the distribution system to avoid the most awful power peaks. In this paper, the DSM algorithm is proposed and implemented in two cases—when both household and EV loads are shifted and only EV loads are shifted. The corresponding results are presented. The impact of the uncoordinatedly scheduled EV loads has been compared with the outcomes obtained by employing the DSM strategy to a residential distribution system and are discussed corresponding to the stability of the distribution system and end-user contentment.

A multi-level Demand-Side Management algorithm for offgrid multi-source systems

The use of renewable sources for electricity supply of islands is faced with technical and economic constraints. To ensure that demand is fully met in the event of low generated power, batteries and gensets are often used and these can be expensive due to fuel import and battery costs. To provide more degrees of freedom for these offgrid networks, a multi-level algorithm based on several Demand Side Management strategies is proposed in this paper. The simulated case study concerns an island supplied by a multi-source system including solar, wind, tidal, wave energies and a battery storage solution. To limit the inconvenience for users, a hierarchical application of the proposed strategies is studied, according to a day-ahead forecast. Strategies based on anticipation are firstly applied for electric room heaters and water heaters in order to use the excess of generated power. For the most critical situations, strategies based on load shifting and load shedding are studied. In these cases, the best solution is found using a genetic algorithm. The application of the proposed Demand Side Management algorithm was found to help reduce the unmet load demand rate and adapt load demand according to the power generated.

Concurrent optimization of thermal and electric storage in commercial buildings to reduce operating cost and demand peaks under time-of-use tariffs

Demand-side management (DSM) in response to market-based electricity tariffs can potentially increase the efficiency and reliability of the electric power grid. This study introduces a novel, one-day-ahead DSM framework which optimizes temperature setpoints and battery dispatch in office buildings, subject to a time-varying and/or demand-based electricity tariff. To reflect real world implementation, our framework operates in two-steps. First, during the passive, battery-only DSM optimization, historical weather and electricity load data for a given building are used to determine its optimal battery capacity. Second, once the battery has been installed, a one-day-ahead, real-time DSM algorithm optimizes both the building’s daily temperature setpoints and the battery's charge/discharge pattern. The optimization objective is to minimize the total operating cost (tariff charges and battery system) while still satisfying occupants’ thermal comfort. Using a case study with a medium-fidelity electric load model for a standard office building, the performance of the proposed framework is validated by quantifying savings in operating cost, reduction of monthly grid peak loads, and the achieved human occupant comfort. To illustrate the advantage of optimizing temperature setpoints and battery dispatch concurrently, the combined performance is compared with that achieved by standalone DSM (i.e., using only battery dispatch or only temperature setpoints). We found that concurrent optimization can reduce a building's monthly peak demand on the grid by up to 26%. Electricity tariff charges are reduced by 11%, more than is required to offset storage costs, thus providing an overall profit to building operators who use such DSM. Payback time is approximately 5 years.

A shrinking-horizon, game-theoretic algorithm for distributed energy generation and storage in the smart grid with wind forecasting

We address the demand-side management (DSM) problem for smart grids where the users have energy generation and storage capabilities, and where the energy price depends on the renewable energy sources and on the aggregate electricity demand. Each user aims at reducing its economic cost by selecting the best energy schedule subject to its local preferences and global restrictions on the aggregate net demand. From a game-theoretic perspective, we model the problem as a generalized Nash equilibrium problem. We propose a shrinking-horizon semi-decentralized DSM algorithm that exploits the most recent forecast on the renewable energy sources to perform real-time adjustments on the energy usage of the users. We investigate the potential of the proposed approach via numerical simulations on realistic scenarios, where we observe improved social welfare compared to day-ahead DSM algorithms.

Personalized real time pricing for efficient and fair demand response in energy cooperatives and highly competitive flexibility markets

This paper contributes to the well-known challenge of active user participation in demand side management (DSM). In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency. Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness. In this paper, a personalized–real time pricing (P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it. Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare (user satisfaction) of electricity consumers. The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.

Development of a Self-Coordinated Algorithm for Demand Side Management in the Case of Aggregated Electric Vehicle in a Grid Integrated System

Electric Vehicles (EV) are now a days proposed to serve the electric power grid bi-directionally by means of consuming energy from grid and also by injecting back the captive energy within the EV battery upon grid requirements. Thus EV and its known variants like Battery Electric Vehicles (BEV) and Plug-in Hybrid-Electric Vehicle (PHEV) possess unique rewards compared to the conventional fossil fueled vehicle. The increasing number of EVs integration with electricity network could have a significant knock on the performance and planning of a power system especially in the demand side management. The recent studies made by the National laboratory of U S Department of Energy clearly mentions the risks involved in EV integration in terms of its peak demand profile and spinning reserve profile. The work in this paper investigates the behavior of different types of EVs & its impact on the load profile in a grid connected system in terms of EV capacity, EV charging levels and EV penetration time. The charging profile thus obtained for the above different cases clearly conveys very significant and relevant information regarding its influence on the peak time demand. The peak time period is extended to late hours respective of the different charging conditions which has a definite impact of DSM. Also, an intelligent algorithm is developed to take care of the Demand Side Management (DSM) issues. For the same, the algorithm inputs the grid as well as the vehicle parameters. The uniqueness of the proposed algorithm is in its ability to avoid the communication complexities with the Independent System Operator (ISO) & aggregator. The work is done after studying relevant market models of EVs having different similar or different characteristics.

Technology-free microgrid modeling with application to demand side management

When trying to model a microgrid, with renewable distributed generation, previous models of generators, loads, electrical networks, and further equipment are necessary, in order to build it.In this paper the authors attempt to put everything in a model in a technology-free way and using a reduced number of data, regardless of the accuracy in representation of loads and generators, only to get a roughly idea about magnitudes, in order to achieve a relatively easy way to experiment some demand side management algorithms, to get a rather general modeling approach, independent of technologies used, and useful to be applied on migrogrid instances. Also a demand side management instance is performed as a possible model application.

Impact of probabilistic small-scale photovoltaic generation forecast on energy management systems

Demand-side Management (DSM) algorithms are exposed to several uncertainties due to their dependency on renewable energy generation forecasts. On the large scale, generation and load forecasts can be relatively accurate, yet on the residential scale, forecasting errors increase due to higher uncertainties. One potential solution is to incorporate a probabilistic PV forecast into an optimal DSM algorithm instead of the existing deterministic PV forecasting algorithms. Hence, in this contribution, a numerical analysis that compares the potential of using a probabilistic PV forecast instead of the conventional deterministic algorithms in a DSM algorithm, is presented. Results show that under different household energy system configurations, the DSM algorithm with the probabilistic PV generation forecast leads to an increase in self-sufficiency and self-consumption by 24.2% and 17.7%, respectively, compared to the conventional deterministic algorithms. These results indicate that probabilistic PV forecasting algorithms may indeed have a higher potential compared to the conventional deterministic ones.

Distributed Optimisation Algorithm for Demand Side Management in a Grid-Connected Smart Microgrid

The contributions of Distributed Energy Generation (DEG) and Distributed Energy Storage (DES) for Demand Side Management (DSM) purposes in a smart macrogrid or microgrid cannot be over-emphasised. However, standalone DEG and DES can lead to under-utilisation of energy generation by consumers and financial investments; in grid-connection mode, though, DEG and DES can offer arbitrage opportunities for consumers and utility provider(s). A grid-connected smart microgrid comprising heterogeneous (active and passive) smart consumers, electric vehicles and a large-scale centralised energy storage is considered in this paper. Efficient energy management by each smart entity is carried out by the proposed Microgrid Energy Management Distributed Optimisation Algorithm (MEM-DOA) installed distributively within the network according to consumer type. Each smart consumer optimises its energy consumption and trading for comfort (demand satisfaction) and profit. The proposed model was observed to yield better consumer satisfaction, higher financial savings, and reduced Peak-to-Average-Ratio (PAR) demand on the utility grid. Other associated benefits of the model include reduced investment on peaker plants, grid reliability and environmental benefits. The MEM-DOA also offered participating smart consumers energy and tariff incentives so that passive smart consumers do not benefit more than active smart consumers, as was the case with some previous energy management algorithms.