Role Of Demand Side Management Research Articles

AI-Driven Solar Energy Generation and Smart Grid Integration A Holistic Approach to Enhancing Renewable Energy Efficiency

This paper comprehensively analyzes AI-driven solar energy generation and smart grid integration, focusing on enhancing renewable energy efficiency. The study examines applying advanced artificial intelligence techniques in optimizing solar power production, forecasting, and grid management. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are evaluated for effectiveness in solar irradiance prediction and PV system performance estimation. The integration of AI in smart grids is explored, highlighting its role in demand-side management, energy storage optimization, and grid stability control. A holistic approach to improving renewable energy efficiency is proposed, encompassing integrated AI frameworks for solar-plus-storage systems, multi-objective optimization techniques for energy management, and AI-enabled microgrids and virtual power plants. The paper also addresses the challenges and future trends in AI application to renewable energy systems, including scalability issues, regulatory considerations, and ethical implications. By leveraging big data analytics and advanced AI algorithms, this research demonstrates the potential for significant improvements in overall system efficiency, reliability, and sustainability of solar energy systems integrated with smart grids.

Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control

Building energy flexibility plays a critical role in demand-side management for reducing utility costs for building owners and sustainable, reliable, and smart grids. Realizing building energy flexibility in tropical regions requires solar photovoltaics and energy storage systems. However, quantifying the energy flexibility of buildings utilizing such technologies in tropical regions has yet to be explored, and a robust control sequence is needed for this scenario. Hence, this work presents a case study to evaluate the building energy flexibility controls and operations of a net-zero energy office building in Singapore. The case study utilizes a data-driven energy flexibility quantification workflow and employs a novel data-driven model predictive control (MPC) framework based on the physically consistent neural network (PCNN) model to optimize the building energy flexibility. To the best of our knowledge, this is the first instance that PCNN is applied to a mathematical MPC setting, and the stability of the system is formally proved. Three scenarios are evaluated and compared: the default regulated flat tariff, a real-time pricing mechanism, and an on-site battery energy storage system (BESS). Our findings indicate that incorporating real-time pricing into the MPC framework could be more beneficial to leverage building energy flexibility for control decisions than the flat-rate approach. Moreover, adding BESS to the on-site PV generation improved the building self-sufficiency and the PV self-consumption by 17% and 20%, respectively. This integration also addresses model mismatch issues within the MPC framework, thus ensuring a more reliable local energy supply. Future research can leverage the proposed PCNN-MPC framework for different data-driven energy flexibility quantification types.

Multi-objective planning of energy hub on economic aspects and resources with heat and power sources, energizable, electric vehicle and hydrogen storage system due to uncertainties and demand response

Nowadays, microgrids and their optimal exploitation based on energy hubs are particularly important. A microgrid is an energy system that supplies electricity to customers with renewable and fossil generation sources. Therefore, the main goal of this work in optimal planning of energy hubs includes how to distribute products such as combined energy and heat sources, hydrogen storage systems, electric vehicles, and controllable loads to examine the role of demand side management and renewables. This research study also aimed at minimizing the cost of power generation in the following hours or days, as well as the spread of environmental pollution. Since various sources of uncertainty such as load, energy price in the wind turbine, and solar cell output power market affect the microgrid's economic optimization, this research study is covered by probabilistic modeling. The hydrogen storage system includes an electrolyzer, hydrogen tanks, and a fuel cell. We apply the load-side response program to flatten the load diagram and optimize the microgrid use. As this model has different objective functions, a multi-objective random algorithm was proposed to solve it. Finally, the proposed model and method for a sample system in different scenarios are discussed. Based on obtained results from all scenarios, the first case reduced the operating cost by 20.75 % and 15.17 % compared to the second and third cases. Similarly, the rate of environmental pollution has decreased by 7.94 % and 2.23 %.

Stochastic modelling of flexible load characteristics of split-type air conditioners using grey-box modelling and random forest method

Split-type air conditioners (ACs) are the major contributors to the total electricity use and peak power demand in residential buildings due to their high penetration. Accurate prediction of their energy consumption plays a significant role in demand side management (DSM), because it can help exploit the demand response (DR) potential of these ACs fully. However, the existing studies on the load characteristics modelling of split-type ACs are still not comprehensive, especially in the consideration of occupant behavior and appliance characteristics. To solve this problem, this study has developed a novel flexible load characteristics model by combining the stochastic method, grey-box modelling (i.e., equivalent thermal parameter (ETP) model) and random forest method. The stochastic method can help capture the dynamic occupants’ energy-related behaviors considering various family structures. Random forest can address the difficulties in simulating the power characteristics of variable-speed ACs under various operating conditions. With the prediction error of 2.8%, the proposed model can effectively characterize the energy performance of split-type ACs at both individual and aggregate levels. To better show the load flexibility of ACs, an investigation on DR potential of split-type ACs was also carried out by resetting temperature setpoints. The result showed that the maximum DR potentials at the scope of 1000 aggregated households are 394.54 kWh and 323.82 kWh on weekdays and weekends, respectively. The developed flexible load characteristics model of ACs can be used by utility companies for DR potential assessment and model-based DR control for a single household or building clusters.

A distributed real-time pricing strategy based on reinforcement learning approach for smart grid

The real-time pricing (RTP) plays an important role in demand side management of smart grid. In this paper, a distributed RTP strategy which takes the interests of both supply and demand sides into consideration is studied. A holistic model focusing on the interactions between users and the power supplier is proposed in the framework of Markov decision process (MDP). The MDP presentation of smart appliances’ operational processes well embodies their characteristics and the energy correlation of adjacent time slots. Different from existing ones, a novel distributed online algorithm based on a reinforcement learning approach is proposed to solve the MDP model without acquisition of the transition probabilities. Through information exchange between users and the power supplier, the real-time electricity price is decided adaptively, meanwhile, the optimal strategy of power supply and consumption is obtained. The specific information of the utility function for each user does not need to be disclosed to the supplier and other users. Simulation results show that the proposed model and algorithm balance energy supply and demand well, and have a good performance in peak shaving and valley filling.

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.

Role of Demand side Management in Reliability of Distribution Network

Role of Demand side Management in Reliability of Distribution Network

Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM

With the deregulation of power market and the increasing penetration of renewable energy, the core role of demand side management (DSM) has become even more prominent. In this sense, there is an urgent need for all market participants to identify the pivotal aspects of electricity market price fluctuation effectively and anticipate its future trend. For certain applications such as DSM, considering the high volatility and nonlinear of real-time electricity price, we can approximate the interval prediction to achieve the multi-classification that relies on critical pattern recognition of entire category of the price sequence. Therefore, this paper presents a study on the utilization of a novel electricity price classification framework which consists of Bayesian extreme learning machine (BELM) model, minimum redundancy maximum relevance (MRMR) algorithm, and multivariate sequence segmentation (MSS). Considering many advantages of deep learning structure in capturing the hierarchical and sophisticated characteristics of multidimensional sequence, the multi-layer BELM (ML-BELM) model is also extended and utilized to the modeling. To demonstrate the potential of the pattern classification framework, the proposed approaches are evaluated using hourly clearing price cases from Canada Ontario and New York electricity market. In particular, we investigate the performance of different classifiers regarding the 3 multi-classification and higher dimensional classification modes with respect to various scenarios in terms of precision, recall, AUC (Area Under roc Curve) score, and F1-measure indicators. The findings suggest that the proposed pattern classification framework can obtain satisfactory forecasting results provided that suitable scheme is utilized to the pattern segmentation and feature ranking process.

Ensuring the reduction in peak load demands based on load shifting DSM strategy for smart grid applications

In recent year the concept of prosumers (energy producer as well as the consumer) is becoming more popular, and on the other side, demand response has gained attention especially in the smart and micro grid systems. In such power networks, the behaviors of prosumers are highly variable due to many factors and few are price depended, incentive depended etc., Due to the highly dynamic nature of the prosumers, the peak load management is becoming crucial in the power system operation and control especially in smart grids, in which demand side management (DSM) application thought to be useful. This paper presents a simulation study on the role of DSM in the peak load management for a residential community. Here, load shitting-based DSM technique is used. This study considered the daily consumption patterns (hour wise) of electricity using the four types of electric loads. Results showed a considerable decrease in the peak load when compared to the load patterns before applying DSM. The average demand per day in summer is 4.521 kW, and in winter is 3.871 kW. The summary of peak load demands per day in kW without DSM are 14.712 kW, and 18.18 kW for summer and winter respectively. With the load shifting based DSM technique, the peak load per day are reduced to 9.6 kW in the summer season and 9.672 kW in the winter season.

Modelling and flexible predictive control of buildings space-heating demand in district heating systems

This paper presents and demonstrates, by numerical simulation, a Mixed-Integer Linear Programming (MILP)-based Model Predictive Control (MPC) strategy for space-heating demand in buildings connected to a district heating system. The proposed MPC deals with space-heating demand with extended flexibility. It exploits thermal inertia, inherently present in the building and its heating system, to optimally plan space-heating load in anticipation of weather conditions and energy cost variations. MPC is based on a reliable Reduced-Order Model (ROM). Heating circuit and internal mass are carefully modelled within the ROM structure since these elements can be used for short-term heat storage and therefore play an important role in demand-side management. As for the model parameters identification, training data is restricted to non-intrusive, easily accessible measurements available at the substation level. The model identification approach and control strategy are applied to a well-insulated radiator-heated case-study building simulator developed in Modelica. Results show that the proposed ROM is reliable enough for an MPC application. Compared to conventional weather-compensation control, flexible MILP-based MPC proved to be cost-efficient, while preserving a decent indoor thermal comfort level.

DSM for all‐day voltage profile improvement in a microgrid

The role of demand side management (DSM) has changed the scenario of control and management in a microgrid. DSM works on real time without utilising any additional equipment in the network. The study proposes an all-day voltage control methodology using DSM. The contribution of this study is threefold. Firstly, a novel method for the day ahead voltage profile improvement is proposed by shifting flexible loads using demand and generation forecast information. Secondly, the proposed method uses only two parameters, the impedance of the network and amount of power injected to the flexible loads, in order to influence the voltage level and to improve overall voltage profile of the system. Thirdly, factor matrix is introduced; it decides which flexible loads to be shifted to regulate voltage within tolerance limits. In order to verify the effectiveness of the proposed voltage control method, it is tested on an low voltage (LV) distribution system considered as a microgrid. The result indicates that when the flexible loads are shifted with proper margin, the voltage of the system is contained within a limit and results in better load distribution throughout the day.

Role of Energy Efficiency and Conservation Including Demand Side Management for Sustainable Development in India

Energy is used to provide cost-effective services meet the objectives of promoting sustainable development. The importance of Energy Efficiency and Conservation (EE and EC) is to conserve depleting energy resources. Energy efficiency in India has been increasing which has led the Government of India through the Energy Conservation Act (ECA) and the Bureau of Energy Efficiency (BEE) to begin several programs. Demand Side Management (DSM) and Energy Efficiency and Conservation are improving the economic growth of Indian utilities.DSM also aimed at promoting the installation and use of energy efficient equipment that consume less power having good quality of output. Energy efficiency is important for sustainable future. Demand Side Management including Demand Response (DR) Activity is utilized to limit the peak electricity demand. DSM helps grid operators to act as virtual power plants and power the utilities to transmit energy when needed through demand response measures. Demand Response (DR) measures can be adapted for a industrial and commercial facility that includes turning off air conditioning, lighting, pumps, and other non-essential equipment. Demand Response is a Demand Side Management (DSM) method in which the end users of electricity are encouraged to take part in dropping the peak load on the system by altering their normal energy consumption schedule. The basic objective of the Agriculture, Municipal and Industrial Demand Side Management (DSM) programmes are to improve the overall energy efficiency of the SLDC (State Load Dispatch Centre) and Electricity Grids which could lead to substantial savings in the electricity consumption, resulting in cost reduction and savings. The target of energy saving can be achieved by implementing acts and policies which leads to state wise DSM Regulations by Regulatory Commissions to the State Power Utilities. The Role of DSM and Energy Efficiency including conservation can fulfill the dreams projects electricity demand in Smart Cities. The Role of State Electricity Regulatory Commissions and Forum of Regulators are very important to make India’s electricity demand in future.

Are building users prepared for energy flexible buildings?—A large-scale survey in the Netherlands

Building energy flexibility might play a crucial role in demand side management for integrating intermittent renewables into smart grids. The potential of building energy flexibility depends not only on the physical characteristics of a building but also on occupant behaviour in the building. Building users will have to adopt smart technologies and to change their daily energy use behaviours or routines, if energy flexibility is to be achieved. The willingness of users to make changes will determine how much demand flexibility can be achieved in buildings and whether energy flexible buildings can be realized. This will have a considerable impact on the transition to smart grids. This study is thus to assess the perception of smart grids and energy flexible buildings by building users, and their readiness for them on a large scale. We attempted to identify the key characteristics of the ideal user of flexible buildings. A questionnaire was designed and administered as an online survey in the Netherlands. The questionnaire consisted of questions about the sociodemographic characteristics of the current users, house type, household composition, current energy use behaviour, willingness to use smart technologies, and willingness to change energy use behaviour. The survey was completed by 835 respondents, of which 785 (94%) were considered to have provided a genuine response. Our analysis showed that the concept of smart grids is an unfamiliar one, as more than 60% of the respondents had never heard of smart grids. However, unfamiliarity with smart grids increased with age, and half of the respondents aged 20–29years old were aware of the concept. Monetary incentives were identified as the biggest motivating factor for adoption of smart grid technologies. It was also found that people would be most in favour of acquiring smart dishwashers (65% of the respondents) and refrigerator/freezers (60%). Statistical analysis shows that people who are willing to use smart technologies are also willing to change their behaviour, and can thus be categorised as potentially flexible building users. Given certain assumptions, 11% of the respondents were found to be potentially flexible building users. To encourage people to be prepared for energy flexible buildings, awareness of smart grids will have to be increased, and the adoption of smart technologies may have to be promoted by providing incentives such as financial rewards.

Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling

The role of Demand Side Management (DSM) with Distributed Energy Storage (DES) has been gaining attention in recent studies due to the impact of the latter on energy management in the smart grid. In this work, an Energy Scheduling and Distributed Storage (ESDS) algorithm is proposed to be installed into the smart meters of Time-of-Use (TOU) pricing consumers possessing in-home energy storage devices. Source of energy supply to the smart home appliances was optimized between the utility grid and the DES device depending on energy tariff and consumer demand satisfaction information. This is to minimize consumer energy expenditure and maximize demand satisfaction simultaneously. The ESDS algorithm was found to offer consumer-friendly and utility-friendly enhancements to the DSM program such as energy, financial, and investment savings, reduced/eliminated consumer dissatisfaction even at peak periods, Peak-to-Average-Ratio (PAR) demand reduction, grid energy sustainability, socio-economic benefits, and other associated benefits such as environmental-friendliness.

Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives

The active management of electricity demand, also referred to as demand side management (DSM), has been recognized as an effective approach to improving power grid performance and consumer benefits. Being large electricity consumers, the power-intensive process industries play a key role in DSM. In particular, enterprise-wide optimization (EWO) for industrial DSM has emerged as a major area of interest for both researchers and practitioners. In this work, we introduce the reader to the fundamentals of power system economics, provide a definition of DSM that reflects more strongly the consumer's perspective, and present a comprehensive review of existing works on EWO for industrial DSM. The review is organized into four parts, which correspond to the four main challenges that we identify as: (1) accurate modeling of operational flexibility, (2) integration of production and energy management, (3) decision-making across multiple time and space scales, and (4) optimization under uncertainty. Finally, we highlight research gaps and future opportunities in this area.

The implications of heat electrification on national electrical supply-demand balance under published 2050 energy scenarios

Published UK 2050 energy scenarios specify a range of decarbonised supply side technologies combined with electrification of transportation and heating. These scenarios are designed to meet CO2 reduction targets whilst maintaining reliability of supply. Current models of the UK energy system either make significant assumptions about the role of demand side management or do not carry out the analysis at sufficient resolution and hence determining the impact of heat electrification on the reliability of supply of the scenarios is not possible. This paper presents a new model that estimates national supply and demand, hour-by-hour. Calculations are based on 11 years of weather data which allows a probabilistic assessment of deficit frequency throughout the day. It is found that achieving demand reduction targets are far more important than meeting electrification targets and that significant adoption of CHP is most likely to deliver a viable energy future for the UK.

Real-Time Pricing Decision Based on Leader-Follower Game in Smart Grid

AbstractThe real-time pricing plays an important role in demand-side management for smart grid. In this paper, we study real-time pricing strategy of electricity retailers by means of game theory in smart grid. The retailers are in the game situation where there is one leader with multi-followers. We propose a real-time electricity demand function and analyze the interactions between the retailers, then obtain its equilibrium solution. The analysis and simulation results of the equilibrium solution show the effectiveness of the proposed method.

Optimal Operation of a Residential Microgrid: The Role of Demand Side Management

In this paper demand side management (DSM), characterized by shifting techniques, is applied to a residen- tial microgrid. It is supposed that the microgrid is managed by a prosumer, a decision maker who manages distrib- uted energy sources, storage units, ICT elements, and loads involved in the grid. DSM is considered as an integral part of the optimal economic short-term management problem such that the allocation of shiftable loads is treated as a vari- able must be determined simultaneously with all the others variables (i.e. energy exchange with the main grid, inter- nal production, charge/discharge of electrical storage units). This paper focuses on the formulation of an economic model includingfunctionallinksbetweenshiftableandshiftedloads the operation energy costs. The model is implemented using IBM ILOG CPLEX an optimization programming language solver. The analysis shows how the variable allocation of shiftable loads is related to the other variables and how all the variables are linked (directly or indirectly) to the energy price and to the other parameters typical of shiftable energy devices. Moreover, the model allows to easily perform sen- sitivity analyses by varying the parameters considered. For instance, transitioning from the pre-shift to post-shift state, an improvement of the economic objective corresponds to an enhancement in the utility load profile. A sensitivity analy- sis is carried out by varying the maximum amount of power exchanged with the main grid. Results provide useful infor- mation to find a compromise between connecting interests. Numerical results are presented and discussed.

Integrating wind power using intelligent electric water heating

Dwindling fossil fuel resources and pressures to reduce greenhouse gas emissions will result in a more diverse range of generation portfolios for future electricity systems. Irrespective of the portfolio mix the overarching requirement for all electricity suppliers and system operators is to instantaneously meet demand, to operate to standards and reduce greenhouse gas emissions. Therefore all electricity market participants will ultimately need to use a variety of tools to balance the power system. Thus the role of demand side management with energy storage will be paramount to integrate future diverse generation portfolios. Electric water heating has been studied previously, particularly at the domestic level to provide load control, peak shave and to benefit end-users financially with lower bills, particularly in vertically integrated monopolies. In this paper a number of continuous direct load control demand response based electric water heating algorithms are modelled to test the effectiveness of wholesale electricity market signals to study the system benefits. The results are compared and contrasted to determine which control algorithm showed the best potential for energy savings, system marginal price savings and wind integration.

The role of demand-side management in the grid integration of wind power

In a scenario of large scale penetration of renewable production from wind and other intermittent resources, it is fundamental that the electric system has appropriate means to compensate the effects of the variability and randomness of the wind power availability. This concern was traditionally addressed by the promotion of wind resource studies and acting in the supply side of energy and using energy storage technologies. However, in electric system planning, other options deserve to be evaluated, namely the options related with the energy demand. The most severe problems due to the wind power intermittence happen during the peak load hours. Thus, instead of trying to replace the lost capacity due to the intermittence, other option is to act in the energy demand side, with the aim of reducing the consumption in such hours. The demand-side management technologies are an option that must be considered to reduce and manage the wind power intermittence. The present paper analyzes the possible impact of demand-side management and demand response, with the aim of enabling the integration of the growing intermittent resources in Portugal.