Demand Side Management Strategies Research Articles

Transition toward net zero emissions - Integration and optimization of renewable energy sources: Solar, hydro, and biomass with the local grid station in central Italy

Clean energy technologies are playing a vital role in energy transition, realization of Net-Zero emission goals, and satisfying increasing energy demands. The objective of this work is to investigate the potentials of renewable energy sources and propose a state-of-the-art new grid connected energy system model, which increases the penetration of renewable energies in the medium-size Municipality of Perugia, Central Italy. The presented energy system model integrates and optimises renewable energy sources mainly consisting of hydro, solar, and biomass, by implementing new dispatch strategies and advanced mathematical model. The developed model maximises the power generation of each source based on the least Net Present Cost and the lowest Levelized Cost of Energy and balances the fluctuating load demand by using combined dispatch strategies. Furthermore, demand-side load management strategies are implemented for efficient energy management. An economic and environmental analysis has been performed to evaluate the impact of the proposed system. The results show that a significant amount of clean energy (33.2 GWh/year) is produced at the lowest Levelized Cost of Energy of 0.067 €/kWh. Moreover, up to 78% of the energy demand of the selected community is covered by renewable energies, which evades 13,452 tons of CO2 emission yearly.

Electric Vehicle Battery-Connected Parallel Distribution Generators for Intelligent Demand Management in Smart Microgrids

Renewable energy penetration increases Smart Grid (SG) instability. A power balance between consumption and production can mitigate this instability. For this, intelligent and optimizing techniques can be used to properly combine and manage storage devices like Electric Vehicle Batteries (EVBs) with Demand-Side Management (DSM) strategies. The EVB helps distribution networks with auxiliary services, backup power, reliability, demand response, peak shaving, lower renewable power production’s climate unpredictability, etc. In this paper, a new energy management system based on Artificial Neural Networks (ANNs) is developed to maximize the performance of islanded SG-connected EVBs. The proposed ANN controller can operate at specified periods based on the demand curve and EVB charge level to implement a peak load shaving (PLS) DSM strategy. The intelligent controller’s inputs include the time of day and the EVB’s State of Charge (SOC). After the controller detects a peak demand, it alerts the EVB to start delivering power. This decrease in peak demand enhances the load factor and benefits both SG investors and end users. In this study, the adopted SG includes five parallel Distribution Generators (DGs) powered by renewable resources, which are three solar Photovoltaics (PVs) and two Wind Turbines (WTs). Sharing power among these DGs ensures the SG’s stability and efficiency. To fulfill demand problem-free, this study dynamically alters the power flow toward equity in power sharing using virtual impedance-based adaptive primary control level. This study proposes a decentralized robust hierarchical secondary control system employing Genetic Algorithm (GA)-optimized Proportional-Integral (PI) controller parameters with fine-grained online tuning using ANNs to restore frequency and voltage deviations. The proposed system is evidenced to be effective through MATLAB simulations and real-time data analysis on the ThingSpeak platform using internet energy technology. Our presented model not only benefits users by enhancing their utility but also reduces energy costs with robust implementation of a control structure by restoring any frequency and voltage deviations by distributing power equally among DGs regardless of demand condition variations.

Demand Side Management and Peer-to-Peer Energy Trading for Industrial Users Using Two-Level Multi-Agent Reinforcement Learning

Over the past few years, with more concerns on energy equity, energy security and environmental sustainability, peer-to-peer (P2P) energy trading market has increasingly attracted attention to energy users. With the rising electricity price and the growing public doubt to unsustainable development, more efforts are given to the integration between P2P energy trading and industrial practice. To date, the current research work has emphasized electricity cost reduction using demandside management (DSM) and P2P energy trading market to promote energy sharing and create a win–win situation for all industrial market participants. However, less attention has been given to evaluating industrial production. With this attention, this paper formulates a cost minimization problem for multiple discrete manufacturers, where DSM and P2P energy trading market are integrated to optimize manufacturing productivity and energy consumption. Moreover, this paper proposes a two-level reinforcement learning (RL) method to obtain the optimal DSM and energy trading strategies for discrete manufacturers. Unlike the existing RL approaches, the proposed method depicts a twolevel learning approach, where the upper-level program suggests a production plan for the manufacturer to improve the cost return, while the lower-level program optimizes the net benefits. Further, to verify the performance of the proposed method, simulations are conducted based on four different case studies, including different weather and load conditions. Numerical results demonstrate that the proposed algorithm provides a better production plan with enhanced learning speed and economic benefits compared to the existing RL approaches.

Optimal scheduling of prosumer's battery storage and flexible loads for distribution network support

AbstractHere, an efficient Energy Management System (EMS) for residential installations is proposed. The EMS handles on site Photovoltaic (PV) power production along with Battery Energy Storage System (BESS) and performs load shifting under a Demand Side Management (DSM) scheme that considers user comfort. The goal of the proposed methodology is the minimization of power exchange between the prosumer and the grid. In this way, the power flow across the Distribution Network (DN) is decreased, thus reducing power losses, and improving the voltage profile along the DN. Moreover, the EMS increases the self‐sufficiency of the residence. The minimization problem is solved via a metaheuristic algorithm, namely Unified Particle Swarm Optimization (UPSO) utilizing real consumption patterns for residential appliances. The latter adds complexity to the problem but offers accurate modelling of the power demand and improves the performance of the EMS scheme. The proposed EMS is locally implemented as a plug‐and‐play scheme and benefits both the prosumer and the DN under low computational burden, proving to be efficient. Simulation results for a case study verify the efficiency of the proposed EMS and the comparison with other similar methods proves its superior performance.

An Integrated Energy Control System to Provide Optimum Demand Side Management of a Grid-Interactive Microgrid

The use of renewables can make it more challenging to maintain a balance between supply and demand in microgrids due to their variable generation profiles. To address this issue, microgrids can be designed to be grid-interactive or to include energy storage units, or both. Additionally, demand-side management (DSM) strategies can be implemented to facilitate control during peak periods. This paper presents a control system for a grid-interactive microgrid with photovoltaic (PV) panels and energy storage units. The proposed system uses a fuzzy-based algorithm to control the energy storage units and provides DSM through the use of a hybrid daily pricing model that combines multi-time rate and inclining block rate pricing methods. To optimize the global maximum power point of the PV panels under partial shading conditions, the microgrid model is tested with several metaheuristic optimization algorithms, including particle swarm optimization (PSO), gray wolf optimization (GWO), and dragonfly algorithm (DA). A hybrid algorithm that combines PSO and DA is also proposed. The resulting integrated control system includes both demand-side and grid-side control operations.

Intelligent Demand Side Management for Exhaustive Techno-Economic Analysis of Microgrid System

In a typical microgrid (MG) structure, the requisite of load varies from hour to hour. On the basis of the rise and fall of the load demand curve, the power system utilities fix the rate of electric power at different times of the day. This process is known as time-of-usage (TOU)-based pricing of electricity. The hourly basis load demand can be categorized into elastic hourly load demand and inelastic hourly load demand. For the duration of the peak hours, when the utility charges more, the elastic loads are shifted to low demand hours by the demand side management (DSM) to save the cost. This rebuilds the total demand model on the pillars of demand price elasticity. Keeping in view the fact that the total load in an hour in an MG structure consists of 10% to 40% of elastic loads, the paper proposes an intelligence-technique-based DSM to achieve reduction in the overall cost of using loads in an MG structure. Seven different cases are studied which cover diverse grid participation and electricity market pricing strategies, including DSM programs. The results obtained for all the MGs showcase the applicability and appropriateness of using the proposed DSM strategy in terms of cost savings.

Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform

Rising electricity prices and the greater penetration of electricity consumption in end-uses have prompted efforts to set up data-driven methodologies to optimise energy consumption and foster user engagement in demand-side management strategies. The performance of energy-management systems is greatly affected by the consumer behaviors and the adopted energy-management methodology. Consequently, it is necessary to develop appliance-level, detailed energy-consumption information models to inform citizens to improve behaviors toward energy use. The goal of the Home Energy Management System (HEMS) is to foster an ecosystem that is energy-optimized and can manage Internet of things (IoT) equipment over its network. HEMS allows consumers to reduce energy costs by adapting their consumption to variable pricing over the day. With the use of descriptive data-mining techniques, we have developed a numerical model that gives consumers access to information on their domestic appliances with regard to the number and duration of operations, cycles disaggregation for appliances that have cyclic operation (e.g., washing machine, dishwasher), and energy consumption throughout various time periods basing on 15-min monitoring data. The model has been calibrated and validated on two datasets collected by ENEA by real-time monitoring of Italian dwellings and has been tested over several appliances showing effective analysis of the energy-consumption patterns. Therefore, it has been integrated in the DHOMUS IoT platform, developed by ENEA to monitor and analyse the energy consumption in dwellings in order to increase citizens’ engagement and awareness of their energy consumption. The results indicate that the developed model is sufficiently accurate, and that it is possible to promote a more virtuous and sustainable use of energy by end users, as well as to reduce the energy demand as required by the current European Council Regulation (EU) 2022/1854.

Prosumer-centric demand side management for minimizing electricity bills in a DC residential PV-battery system: An Australian household case study

Technological advancements lead to the development of a home energy management system (HEMS) to perform demand side management (DSM) in residential houses. The current study proposes a DSM strategy to minimize electricity bills in a DC residential house considering the predictions of electrical load consumption and solar photovoltaic (PV) power based on long short-term memory networks. A simulation study compares the proposed DSM strategy against four alternate scenarios, the result shows that the proposed DSM strategy has the lowest annual energy cost increment (5.81%) compared to the benchmark scenario where it is assumed to have the ideal predictions of the load consumption and solar PV power. It also shows that the predictions of the load consumption and solar PV power are essential for economically sensible DSM, however further analysis reveals that the prediction errors do not correlate with the differences in daily energy costs (or daily peak powers) resulting from the proposed DSM strategy and benchmark scenario. Finally, the proposed DSM strategy is experimentally validated on a small-scale hardware-in-the-loop test platform to demonstrate the feasibility of its practical implementation in a HEMS.

A Review of Demand Side Management Strategies and Electricity Tariffs in Distributed Grids

A major challenge in renewable energy planning and integration with existing systems is the management of intermittence of the renewable resources and customer demand uncertainties. In emerging distributed grids, state-of-the-art optimization techniques were used for cost and reliability objectives. In the existing literature, power dispatch and demand side management schemes were implemented for various techno-economic objectives. In renewable energy-based distributed grids, power dispatch is strategic to distributed grid operations. However, demand side management is preferred, as it allows more options for customer participation. Moreover, the demand side management can simply follow supplies. This paper investigates the functions of demand side management as it affects the planning and operations of renewable energy-based distributed grids. The traditional demand side management strategies were inflexible and affect customer comforts. The time-of-use methods on the other hand are utility-centered and do not allow customer participation. Hence, integration of demand side management with tariff regimes through hybridizations such as time-of-use based real-time pricing were conceptualized for improved economic performance, reliability enhancement, and mitigation of environmental implications.

Deep reinforcement learning for energy trading and load scheduling in residential peer-to-peer energy trading market

The popularization of solar generation enables residential households to supply their loads and trade the surplus energy through residential peer-to-peer (P2P) energy trading market. In a residential community with multi-households, households are able to determine demand-side management (DSM) and energy trading strategies to reduce electricity cost. However, residential users often decide market participation biddings according to their experience and guesses on other households’ biddings, and it is difficult to know how far these biddings are from the best possible market biddings achievable by the P2P market trading mechanism. Targeting the maximum cost savings achievable from this P2P market, this paper focuses on how to obtain such a maximum saving under optimal energy trading and DSM strategies using a multi-agent deep reinforcement learning approach to obtain the optimal solution. In this regard, the energy trading and DSM problem for residential households is formulated as a partially observable Markov decision process (POMDP). Then, a reinforcement learning algorithm with decentralized training and execution approach is adopted in the proposed POMDP problem to fit the policy function and value function, which are trained against each other to learn the optimal strategies by trial-and-error. The simulation result validates that by using the proposed multi-agent reinforcement learning method to optimize the P2P energy trading and DSM strategies, the average reward for households can be significantly improved by 8.52% and 14.03%, respectively, compared with other two state-of-the-art deep reinforcement learning methods.

A Novel Temperature-Independent Model for Estimating the Cooling Energy in Residential Homes for Pre-Cooling and Solar Pre-Cooling

Australia’s electricity networks are experiencing low demand during the day due to excessive residential solar export and high demand during the evening on days of extreme temperature due to high air conditioning use. Pre-cooling and solar pre-cooling are demand-side management strategies with the potential to address both these issues. However, there remains a lack of comprehensive studies into the potential of pre-cooling and solar pre-cooling due to a lack of data. In Australia, however, extensive datasets of household energy measurements, including consumption and generation from rooftop solar, obtained through retailer-owned smart meters and household-owned third-party monitoring devices, are now becoming available. However, models presented in the literature which could be used to simulate the cooling energy in residential homes are temperature-based, requiring indoor temperature as an input. Temperature-based models are, therefore, precluded from being able to use these newly available and extensive energy-based datasets, and there is a need for the development of new energy-based simulation tools. To address this gap, a novel data-driven model to estimate the cooling energy in residential homes is proposed. The model is temperature-independent, requiring only energy-based datasets for input. The proposed model was derived by an analysis comparing the internal free-running and air conditioned temperature data and the air conditioning data for template residential homes generated by AccuRate, a building energy simulation tool. The model is comprised of four linear equations, where their respective slope intercepts represent a thermal efficiency metric of a thermal zone in the template residential home. The model can be used to estimate the difference between the internal free-running, and air conditioned temperature, which is equivalent to the cooling energy in the thermal zone. Error testing of the model compared the difference between the estimated and AccuRate air conditioned temperature and gave average CV-RMSE and MAE values of 22% and 0.3 °C, respectively. The significance of the model is that the slope intercepts for a template home can be applied to an actual residential home with equivalent thermal efficiency, and a pre-cooling or solar pre-cooling analysis is undertaken using the model in combination with the home’s energy-based dataset. The model is, therefore, able to utilise the newly available extensive energy-based datasets for comprehensive studies on pre-cooling and solar pre-cooling of residential homes.

Space heating demand profiles of districts considering temporal dispersion of thermostat settings in individual buildings

Future energy infrastructures are expected to be smart, with the application of energy storage and demand-side management strategies. To size the energy storage and assess the flexibility of the heat demand in a district, insight into its heat demand profile is a prerequisite.A tool was developed to generate the hourly space heating demand of a district. A crucial element in the calculation is the behaviour of occupants, particularly the temporal dispersion of thermostat settings in individual buildings. This dispersion smooths out the morning (and evening) peaks in the space heating demand of a district when occupants turn up their thermostats.Hourly gas consumption data were obtained of 8077 buildings with gas fired boilers in 2020. A dynamic resistance capacitance (RC) building model of a single aggregated building representing the 8077 dwellings was used to calculate their space heating demand profile in 2020. A comparison with the actual gas consumption data (corrected for domestic hot water use and cooking) allowed the estimation of the temporal thermostat settings, the dispersion thereof and the thermostat temperature levels of buildings in the district. The combined space heating demand profile of the individual buildings calculated using the same RC building model (each with its own thermostat settings) reproduced the actual space heating demand profile relatively well.

Demand side management as a mandatory inclusion for economic operation of rural and residential microgrid systems

The load requirement of a microgrid system fluctuates on an hourly manner. As the load demand rises and falls, utilities set various rates at different hours of the day, which is known as the time-of-usage (TOU) based electricity pricing. The two types of load that make up the microgrid system's hourly demand are elastic and inelastic. Demand-side management (DSM) restructures the entire demand model based on demand-price elasticity by moving elastic loads from peak load hours to those hours when the utility charges less. This research employs a hybrid intelligence method to reduce the total cost of three microgrid systems taking into account the implementation of DSM. Practical complexities like unit-commitment of the dispatchable fossil fuelled generators were considered. Numerical results project noticeable savings ranging from 8% to 18% for all the test systems when the DSM strategy was incorporated. A detailed comparative analysis with the results available in the literature corroborates the effectiveness of the proposed hybrid approach. Measures of central tendencies, non-parametric statistical analysis and algorithm execution time show better results of the hybrid optimization tool presented in this paper.

DEMAND SIDE MANAGEMENT AND DEMAND RESPONSE FOR OPTIMAL ENERGY USAGE: AN OVERVIEW

Being more than a decade old idea, the Demand-side management (DSM) is among the most vital part of the modern smart grid. DSM enables the utilities to minimize the gap between the supply and the demand by optimizing their pattern of user loads. At the same time, it helps them in achieving economic and energy efficient systems by reducing the peak to average (PAR). The implementation of DSM programs by the utilities could help them in improving their reliability, power quality, energy, and system efficiency. On the other hand, customers could be use it to improve their load profile, reduce the peak demands, save energy, and motivate them use more and more renewable energy. Thus, both the utilities and the consumers get benefitted by the implementation of DSM program in the smart grid. This study tries to understand the application of energy efficient policies and the demand response techniques with various DSM strategies. The study mainly focuses on the various characteristics that would lead to effective implementation of DSM programs with particular attention of the residential energy demand. Also, there will be a focus on enhancement of energy efficiency leading to more effective policy responses. The researchers could find this study very helpful as it could be employed to maximize the utility profits, the total load factor, peak demand and also minimize the consumer usage bills.

Scheduling strategy for integrated energy system considering value optimizationand two-way interaction between supply and demand

The integrated energy system (IES) provides an open and efficient platform for energy consumption. Under the background that higher requirements are raised on the optimal scheduling strategy for regional IES, which includes a variety of complex resources, and based on the technical foundation of demand-side management strategies, an optimal scheduling model of IES considering two-way interaction between supply and demand is established in this paper. The calculation results from the selected example show that the proposed method can realize the coordination among multiple participants of the IES and can achieve value optimization with a better overall economy than the traditional method.

Risk-based day-ahead planning of a renewable multi-carrier system integrated with multi-level electric vehicle charging station, cryptocurrency mining farm and flexible loads

Integration of different infrastructures has become an interesting subject due to many advantages such as high flexibility and reliability. However, interconnection and coordination of components create critical challenges. In this paper, the day-ahead planning of a renewable multi-carrier system composed of various resources, loads and storage units is optimized considering multi-level electric vehicle charging station and cryptocurrency mining farm as the electrical loads with high energy consumption. Since the instability of renewable resources in such structures leads to increasing market trading and uncertain decisions, different scenarios are generated for wind speed, solar radiation and price of markets. Then, the conditional value-at-risk metric is used to simultaneously evaluate the risk of uncertainties. Moreover, the demand side management strategies are taken into consideration to analyze the role of flexible electrical, heating, cooling and vehicle loads in energy management. A sensitivity analysis is eventually proposed to indicate the effect of various parameters. The results for the risk-averse strategy approve that increasing the robustness against the worst-case scenarios reduces the profit by about 198.97 $. Although the crypto mining farm consumes high electrical energy equal to 7605 kWh, it increases the profit by about 277.34 $. The simulations also validate that implementing the demand response program improves the system flexibility and increases the profit by about 936.57 $. Furthermore, the sensitivity analysis shows the notable impact of initial state of components on the objective function.

Systematic Review of Demand-Side Management Strategies in Power Systems of Developed and Developing Countries

Balancing electricity demand and supply remains a significant challenge for the power systems in developing countries, such as Nigeria. In Nigeria, there is a shortage of adequate power supply, and demand-side management (DSM) plays a minor role in the power balancing mechanism with load shedding being widely used. The paper aims to review and compare various existing and emerging DSM strategies in developing countries. An extensive and systematic review was conducted to evaluate potential solutions using DSM to increase the overall energy efficiency in the Nigerian electricity market. This study found that, although the technical and economic potentials of DSM vary in developed countries, the uptakes of DSM have been severely hampered hence preventing the full exploitation and utilisation of the full potential of DSM. The initiatives of a DSM model in Nigeria and other developing countries can play a significant role in addressing demand and supply challenges but an upgrade of the energy infrastructures, a reform of the market structure and the provision of financial incentives are required to allow for wide implementations of DSM strategies in developing countries.

Performance assessment of an electrode boiler for power-to-heat conversion in sustainable energy districts

Renewable energy production is rapidly increasing, but it is not demand-oriented, and this makes challenging its integration into national distribution grids. Sector coupling strategies (i.e., power-to-heat) represent a solution to this issue since they enable demand-side management strategies by increasing energy flexibility. Among the others, electric boilers are commonly used to convert electricity into heat, but they are characterized by high radiative heat losses (i.e., the high surface temperature of components) with the consequent risk of unwanted combustion events. Electrode boilers that are based on ion acceleration principles can reduce this risk since they usually operate at a lower temperature. This study focuses on the performance assessment of an electrode boiler. The power-to-heat conversion efficiency is experimentally determined for three configurations of the apparatus, which are determined by varying the velocity of the pumped fluid. The II and III velocity levels are respectively assessed in test one and test two, while the “underfloor heating system” setting (i.e., constant hydraulic head) is evaluated in test three. The electrode boiler is activated within a closed environment equipped with sensors to monitor the air temperature, the surface temperature of the walls, and the consumed electricity. The heat losses and gains are estimated to quantify the heat produced by the electrode boiler. The configuration of the electrode boiler from test one is the most effective, with an efficiency of 97 %. The boiler converted 1444Wh of electricity into 1404 Wh of thermal energy, heating the pumped fluid by up to 55 °C. Such a temperature regime makes the electrode boiler ideal for being coupled to high-temperature radiators in the renovation of obsolete buildings while reducing fire-related risk compared to traditional electric boilers.

Structural Ensemble Regression for Cluster-Based Aggregate Electricity Demand Forecasting

Accurate electricity demand forecasting is vital to the development and evolution of smart grids as well as the reinforcement of demand side management strategies in the energy sector. Since this forecasting task requires the efficient processing of load profiles extracted from smart meters for large sets of clients, the challenges of high dimensionality often lead to the adoption of cluster-based aggregation strategies, resulting in scalable estimation models that operate on aggregate times series formed by client groups that share similar load characteristics. However, it is evident that the clustered time series exhibit different patterns that may not be processed efficiently by a single estimator or a fixed hybrid structure. Therefore, ensemble learning methods could provide an additional layer of model fusion, enabling the resulting estimator to adapt to the input series and yield better performance. In this work, we propose an adaptive ensemble member selection approach for stacking and voting regressors in the cluster-based aggregate forecasting framework that focuses on the examination of forecasting performance on peak and non-peak observations for the development of structurally flexible estimators for each cluster. The resulting ensemble models yield better overall performance when compared to the standalone estimators and our experiments indicate that member selection strategies focusing on the influence of non-peak performance lead to more performant ensemble models in this framework.

A demand side management approach to increase self-consumption in buildings

There is a growing interest in increasing the presence of renewable energy in the electric network. Photovoltaic production from grid-connected systems is leading this growth in terms of households. Alongside this development, concern about network security has emerged, because excesses of intermittent renewable energy on the grid could exceed voltage limits. Self-consumption, understood as the capacity of the producer to consume his or her own production, can partially solve these problems. Thermostatic controllable loads, such as heating and cooling, represent 50% of the total amount of energy consumed by buildings; the proper allocation of these loads could be a driving force for self-consumption. In this study, a demand side management strategy is proposed based on a building energy model equipped with an inverter heat pump coupled with a photovoltaic plant. The goal is to maximize the use of local energy from the photovoltaic plant (self-consumption), reducing the export and import of energy to and from the grid. This goal is achieved by optimizing the set-points in each room. An array of optimal set-points over six years is presented. The results show the capacity of the methodology to match similar values of self-consumption (70% in winter and 50% in summer) obtained by strategies based on chemical batteries. The findings are shown in an energy matching chart at different levels of detail (yearly and monthly). Color bubbles are added to the matching chart to help visualize the unmatched energy of the system graphically. In comparison with actual model predictive control technologies, this study’s strategy offers great simplicity and a large saving in computational time.