Demand Side Management Techniques Research Articles

A novel billing approach for fair and effective demand side management: Appliance level billing (AppLeBill)

Demand side management (DSM) is an important part of smart electrical grid studies due to high energy efficiency it provides. Effective DSM techniques are hinged on participation level of the customers and control manner of their loads so if there is no fair and appropriate management, customers will not embrace demand side management applications. Besides, the customers might not participate to the demand response process willingly if their comforts are not considered. Therefore, the DSM systems must consider fairness and customer’s comfort as well as distribution system objectives. Multifarious DSM systems have been proposed in the literature, however, the fair billing has not included as a parameter in most of these studies. This paper introduces a novel billing approach based upon appliance level billing (AppLeBill) for fair and effective demand side management. The proposed AppLeBill idea offers a billing strategy such that time-shiftable appliances (TSAs) in a residence may have a separate billing rate rather than a single billing rate for total electricity usage. The proposed AppLeBill concept includes three different schemes which are explained and simulated in order to compare each other in terms of their potential benefits.

A long short-term memory artificial neural network to predict daily HVAC consumption in buildings

In this paper, the design and implementation process of an artificial neural network based predictor to forecast a day ahead of the power consumption of a building HVAC system is presented. The featured HVAC system is situated at MagicBox, a real self-sufficient solar house with a monitoring system. Day ahead prediction of HVAC power consumption will remarkably enhance the Demand Side Management techniques based on appliance scheduling to reach defined goals. Several multi step prediction models, based on LSTM neural networks, are proposed. In addition, suitable data preprocessing and arrangement techniques are set to adapt the raw dataset. Considering the targeted prediction horizon, the models provide outstanding results in terms of test errors (NRMSE of 0.13) and correlation, between the temporal behavior of the predictions and test time series to be forecasted, of 0.797. Moreover, these results are compared to the simplified one hour ahead prediction that reaches nearly optimal test NRMSE of 0.052 and Pearson correlation coefficient of 0.972. These results provide an encouraging perspective for real-time energy consumption prediction in buildings.

A DSM Approach for Distribution Systems with High Wind Power Penetration

The basic objectives of demand side management (DSM) are shifting load from peak hours to off-peak hours and reducing consumption during peak hours. The DSM operation is cleared when deregulated electricity market is considered where the retailer purchases electricity from the electricity market to cover the end users requirements of energy. In this paper, DSM techniques (load shifting and peak clipping) are used to maximize the profit for retailer company by reducing total demand at peak hours and achieve an optimal daily load schedule using linear programing (LP) and genetic algorithm (GA). These techniques are implemented on the 33-bus radial network included wind generation penetration. A short term artificial neural network technique (ANN) is used to get forecasted wind speed and forecasted users load for date 25-March-2018. The ANN uses an actual hourly load data and an actual hourly wind speed data. Then the forecasted data is used in the optimization to get optimal daily load schedule to maximize the profit for retailer company. Finally, comparison is carried out between profit using LP and GA. The optimized DSM succeeded to increase the profits of the company by around 4.5 times its old profit using LP and around 2.5 times using GA.

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.

Evaluation of the Techno-Economic Benefits of Shifting Air Conditioning Loads from Evening and Day Peaks to Off Peak Hours A Case Study

With the development of industries and changes in living standards of the society, demand for electricity is rapidly increasing. In order to maintain the demand supply balance and to provide uninterrupted supply, utilities have to meet electricity demand in the most economical way. Building new power plants is not always the most economical solution. The trend now, is towards reducing and controlling the demand through Demand Side Management (DSM) techniques which lead to almost always economical and environment-friendly solutions. In this paper, Heating, Ventilation and Air Conditioning (HVAC) system of Cinnamon Lakeside Hotel is analysed to identify the potential DSM options that can be implemented. Thermal Energy Storage (TES) was selected as the DSM option to store cooling load during off-peak hours of the day where electricity is fairly cheap and use it during peak and day hours when the electricity is expensive. Technical viability and potential saving that can be achieved through TES in hotel sector of Sri Lanka is further analysed.

Smart Scheduling of a Batch Manufacturer’s Operations by Utilization of a Genetic Algorithm to Minimize Electrical Demand

Abstract Power utilities currently manage unpredictable electrical demand through the use of fast-ramping plants and punitive demand fees. Unpredictable demand makes it more difficult to onboard variable renewable energy sources, such as solar and wind, because of their intermittency, which can contribute to grid instability. Optimal utilization of variable renewable energy sources requires a flexible, resilient electrical grid and thus a transformation of the current system to a proposed “smart grid” that would adapt in real time to grid signals. Batch manufacturers are well suited to reduce the burden of intermittency by considering demand side management techniques when scheduling process operations. The goal of such participation is a reduction in electrical demand and associated costs for both the facility and the utility. This study investigates how a novel application of the genetic algorithm could be used to schedule a batch manufacturer’s operations in a manner that reduces overall peak demand and is compatible with process constraints. The genetic algorithm is chosen because it is highly efficient at finding minima and handling complex constraints and large data sets. A major highlight of this article is the use of measured demand profiles to log real-time energy consumption of process equipment. The scenarios investigated use a genetic algorithm to determine the optimum scheduling when given a fixed set of interdependent operations that must be executed within the day. All investigated scenarios have a reduction in peak demand after rescheduling via the genetic algorithm. Baseline operations demonstrated reductions between 13 % and 28 %, with an average reduction of 21 %. The results of this study demonstrate that automated load shifting can easily be applied to an industrial facility and paves the way for future work to investigate the integration of scheduling algorithms with process control systems.

Demand Side Management Technique to Optimally Schedule Electric Vehicle Loads in Smart Grid

Demand-side management (DSM) in smart grids helps the problem of reducing peak load of utilities during certain hourly periods. Based on DSM techniques, peak load hours can be equalized to non-peak load hours therefore users will have less bill payments. In this paper optimal scheduling of Electric Vehicles (EVs) is done based on an objective function formulated to minimize the load variations. Firstly, hourly consumption of load during a day at Koneru Lakshmaiah Education Foundation is considered, EVs load is assumed and flattened the aggregated load curve by optimally scheduling the EVs during off peak hours.

Development of Optimized Methodology for Improvement Domestic Energy Management

Electrical energy management (EEM) is an object that has proceeds appointed importance in the 21th- century in order to its assistance to economic development and ecological ascertainment. “EEM” may be perfected on the supply side “(SS)” or demand side “(DS)”. On the supply side, “EEM” is cultivated when: There is an outgrowth desire “(demand requirement is higher than supply)”. “EEM” assists to suspend the design a resent generation station. On the “DS”, “EEM” is used to minimize the cost of electrical energy consumption and the interrelated forfeitures. The technique utilized for “EEM” is demand side load management that plan at ending valley filling, peak clipping and strategic preservation of electrical systems [1]. Seeming new inventions like “distributed generation (DG)”, “distributed storage (DS)” and “DSLM” will modify the method we use and generate energy. A smart grid (SG) is an electrical network that manages electricity demand in an unstoppable sustainable, reliable and economic manner. A smart grid uses smart net meters to overcome the sickliness of traditional electrical grid. “(DSM)” is a vital advantage of “(SG)” to progress power efficiency, minimize the peak average load and minimize the cost. From basic purposes of DSM is shifting load from peak hours to off-peak hours and reducing consumption during peak hours. Generally, a deregulated grid system is considered where the retailer purchases electricity from the electricity market to cover the end users’ energy need. In this research, Demand Side Management (DSM) techniques (load shifting and Peak clipping) are used to maximize the profit for Retailer Company by reducing total power demand pending peak demand periods and achieve an optimal daily load schedule using linear programming method and Genetic Algorithm. This method is performed on the 69-bus radial network. Also, a short term Artificial Neural Network technique is used to get forecasted wind speed, solar radiation and forecasted users load for date 15-Aug-2019. The neural network here uses an actual hourly load data, actual hourly wind speed and solar radiation data. Then the forecasted data is used in the optimization to get optimal daily load schedule to maximize the profit for Retailer Company. Then comparison between profit using linear programing and genetic algorithm are made. The optimized DSM succeeded to maximize the profits of the company.

Home Energy Management System for a domestic load center using Artificial Neural Networks towards Energy Integration

This paper titled ‘Home Energy Management System (HEMS) for a domestic load center using Artificial Neural Networks towards Energy Integration’ focuses on implementing intelligent integration of Distribution Generation Systems (DGS) for domestic load. The proposed energy integration by HEMS, is executed by implementing a Load Management Algorithm (LMA), which functions based on ANN forecast models. Demand Side Management (DSM) techniques are the back bone for LMA proposed in this paper. Historical temperature data and electrical load data of a domestic load center at Municipality of Birmingham, City of Alabama, USA, is considered to implement the proposed LMA. Two subroutine programs/algorithms are designed to implement the proposed LMA. First is of implementing Load Priority Techniques, to assign Load Priority for the loads for the hour based on the temperature forecast. Temperature forecast is done using Nonlinear Auto Regression with Exogenous Inputs (NARX) ANN time series model. And this is termed as Load Priority Assignment Algorithm (LPAA). Second is of predicting Threshold Power (PTh) for the hour as per load priority assigned by LPAA, this is termed as estimation of PTh. Big data driven Nonlinear Auto Regression with Exogenous Inputs (NARX) Neural Net based temperature forecasting model is implemented and used to assign hourly priority for all the appliances/loads. Big data driven Artificial Neural Network (ANN) based load power forecasting model is implemented and used to predict hourly PTh, LMA proposed for HEMS is implemented with step by step comparison of current load demand with PTh and transferring the loads between the energy sources, according to the load priority assignment from LPAA. Proposed LMA based HEMS contributes to enhance the penetration of nonconventional DGS into domestic load sector. Simulation of proposed HEMS is implemented in MATLAB-Simulink environment using MATLAB 2018b. Simulation model is embedded into target hardware: Arduino 2560 by enabling serial communication protocols between MATLAB-SIMULINK and ARDUINO. Hence the experimental setup of HEMS would be functional to prove simulation results and to transfer the loads in real time. Working Hardware model of the HEMS at domestic load center is fabricated using simple and cost effective components such as current sensor ACS712, Arduino Mega 2560, relays and relay driver circuit.

Perspectives and Intensification of Energy Efficiency in Commercial and Residential Buildings Using Strategic Auditing and Demand-Side Management

With the ever-growing power demand, the energy efficiency in commercial and residential buildings is a matter of great concern. Also, strategic energy auditing (SEA) and demand-side management (DSM) are cost-effective means to identify the requirements of power components and their operation in the energy management system. In a commercial or residential building, the major components are light sources and heating, ventilation, and air conditioning. The number of these components to be installed depends upon the technical and environmental standards. In this scenario, energy auditing (EA) allows identifying the methods, scope, and time for energy management, and it helps the costumers to manage their energy consumption wisely to reduce electricity bills. In the literature, most of the traditional strategies employed specific system techniques and algorithms, whereas, in recent years, load shifting-based DSM techniques were used under different operating scenarios. Considering these facts, the energy data in a year were collected under three different seasonal changes, i.e., severe cold, moderate, and severe heat for the variation in load demand under different environmental conditions. In this work, the energy data under three conditions were averaged, and the DSM schemes were developed for the operation of power components before energy auditing and after energy auditing. Moreover, the performance of the proposed DSM techniques was compared with the practical results in both scenarios, and, from the results, it was observed that the energy consumption reduced significantly in the proposed DSM approach.

A survey of modelling and smart management tools for power grids with prolific distributed generation

Abstract Over the past few decades, the proliferation of distributed generation (DG), fuelled chiefly by environmental concerns, has brought about fundamental changes to the structure and behaviour of the power grid. These changes have spawned new challenges in the way with which the power grid should be managed. As a result, the development of new modelling approaches, as well as management techniques deemed “smart”, is called for to better monitor and control this increasingly complex system by taking into account the specificities of distributed generators (DGs) and their impact on the grid. This paper explores the evolution of power grids into “smart grids” in the light of the penetration of distributed generation and the features that ensue. Then, it gives an overview of notable works in the literature aimed at the modelling and control of power grids within this framework. The reader is provided with an in-depth analysis of a variety of methods through the lens of the applications for which they are most suited. Though well-established methods that have been in use for infrastructure planning for decades, such as optimal power flow (OPF), remain reliable tools, cutting-edge research in the field of multi-agent systems (MAS) opens the door to putting a myriad of complex numerical algorithms in the service of power grid monitoring and control. In addition, a special interest is paid to demand-side management (DSM) techniques.

An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory

The feature of bidirectional communication in a smart grid involves the interaction between consumer and utility for optimizing the energy consumption of the users. For optimal management of the energy at the end user, several demand side management techniques are implemented. This work proposes a home energy management system, where consumption of household appliances is optimized using a hybrid technique. This technique is developed from cuckoo search algorithm and earthworm algorithm. However, there is a problem in such home energy management systems, that is, an uncertain behavior of the user that can lead to force start or stop of an appliance, deteriorating the purpose of scheduling of appliances. In order to solve this issue, coordination among appliances for rescheduling is incorporated in home energy management system using game theory. The appliances of the home are categorized in three different groups and their electricity cost is computed through the real-time pricing signals. Optimization schemes are implemented and their performance is scrutinized with and without coordination among the appliances. Simulation outcomes display that our proposed technique has minimized the total electricity cost by 50.6% as compared to unscheduled cost. Moreover, coordination among appliances has helped in increasing the user comfort by reducing the waiting time of appliances. The Shapley value has outperformed the Nash equilibrium and zero sum by achieving the maximum reduction in waiting time of appliances.

Integration of Renewable Energy Sources by Load Shifting and Utilizing Value Storage

The Integration of renewable energy resources suffers from two fundamental issues: variability, and uncertainty of power output. These issues hinder the integration of renewable resources with the existing grid. This paper addresses these issues and proposes a new methodology to minimize the impact of intermittency by offering an alternative approach for energy storage. The concept of value storage is introduced as an alternative to energy storage to replace the typical large-scale battery energy storage system. The concept refers to the storage of excess renewable energy as products from industrial loads instead of the energy itself which enables a demand side management technique to be applied, namely, load shifting for some industrial plants. A hybrid Photovoltaic-wind turbine generator PV-WTG and storage system is proposed to penetrate the existing electric grid, with significant cost savings by displacing the conventional energy generation in a fossil fuel-rich location. A size optimization based on differential system cost is formulated and solved by an enhanced genetic algorithm technique. Uncertainty impact studies were done by incorporating multiple scenarios and comprehensive sensitivity analysis.

Role of Demand Side Management Techniques in Reducing Electricity Demand of Residential Users

Demand of electrical energy is growing day by day worldwide. To meet this increasing demand, generation is needed to be increased subsequently. Increasing generation is not an easy task as it may require setting up new generating units, changing transmission lines, control equipments, etc. Moreover, increased generation also causes increased environment pollution. An alternate approach that can create balance between demand and supply of electricity without increasing generation is demand side management (DSM). Furthermore, demand side management has the potential to reduce the use of energy resources resulting in less environment pollution. In this paper, three DSM techniques: using solar system, load limiting, deliberate load reduction are applied for residential users of Dhaka, Bangladesh and the results are compared with two traditional techniques: energy efficiency, direct load control. It has been found that by using solar system at home significant amount of electrical energy can be saved.

Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN

Air Conditioners (AC) impact in overall electricity consumption in buildings is very high. Therefore, controlling ACs power consumption is a significant factor for demand response. With the advancement in the area of demand side management techniques implementation and smart grid, precise AC load forecasting for electrical utilities and end-users is required. In this paper, big data analysis and its applications in power systems is introduced. After this, various load forecasting categories and various techniques applied for load forecasting in context of big data analysis in power systems have been explored. Then, Levenberg–Marquardt Algorithm (LMA)-based Artificial Neural Network (ANN) for residential AC short-term load forecasting is presented. This forecasting approach utilizes past hourly temperature observations and AC load as input variables for assessment. Different performance assessment indices have also been investigated. Error formulations have shown that LMA-based ANN presents better results in comparison to Scaled Conjugate Gradient (SCG) and statistical regression approach. Furthermore, information of AC load is obtainable for different time horizons like weekly, hourly, and monthly bases due to better prediction accuracy of LMA-based ANN, which is helpful for efficient demand response (DR) implementation.

A\xa0pricing scheme for electric utility's participation in day-ahead and real-time\xa0flexibility energy markets

Liberalized electricity markets, smart grids and high penetration of renewable energy sources (RESs) led to the development of novel markets, whose objective is the harmonization between production and demand, usually noted as real time of flexibility markets. This necessitates the development of novel pricing schemes able to allow energy service providers (ESPs) to maximize their aggregated profits from the traditional markets (trading between wholesale/day-ahead and retail markets) and the innovative flexibility markets. In the same time, ESPs have to offer their end users (consumers) competitive (low cost) energy services. In this context, novel pricing schemes must act, among others, as automated demand side management (DSM) techniques that are able to trigger the desired behavioral changes according to the flexibility market prices in energy consumption curves (ECCs) of the consumers. Energy pricing schemes proposed so far, e.g. real-time pricing, interact in an efficient way with wholesale market. But they do not provide strong enough financial incentives to consumers to modify their energy consumption habits towards energy cost curtailment. Thus, they do not interact efficiently with flexibility markets. Therefore, we develop a flexibility real-time pricing (FRTP) scheme, which offers a dynamically adjustable level of financial incentives to participating users by fairly rewarding the ones that make desirable behavioral changes in their ECCs. Performance evaluation results demonstrate that the proposed FRTP is able to offer a 15%–30% more attractive trade-off between the stacked profits of ESPs, i.e. the sum of the profits from retail and flexibility markets, and the satisfaction of the consumers.

Multi-objective Harmony Search Algorithm for Dynamic Optimal Power Flow with Demand Side Management

In modern power systems, efficient methods like demand side management (DSM) are needed for handling the peak load management problem. An intelligent DSM approach not only gives advantages to utilities but also provides indirect benefits to generating companies. This article introduces a combined model of multi-objective dynamic optimal power flow (MODOPF) and a game theory-based DSM technique. In this article, a single utility company and multiple residential energy consumers are considered for the game theory-based DSM technique. Here, the day ahead load shifting DSM technique is implemented with the help of a day-ahead pricing strategy and an energy consumption game. The total generation cost and transmission losses are considered as the main objectives in the proposed MODOPF problem. These two objectives were optimized individually and simultaneously using harmony search (HS) algorithm with different DSM participation levels for the investigation of generation side benefits. The proposed combined model was tested on two different test systems such as IEEE 30 bus and 118 bus. From the simulation results, it is clear that the combined model consisting of MODOPF and DSM is able to achieve a better economic and secure operation when compared to MODOPF alone.

Optimal Multi-Timescale Demand Side Scheduling Considering Dynamic Scenarios of Electricity Demand

In this paper, an optimal multi-timescale demand side scheduling framework, i.e., the combination of week-ahead and day-ahead, for industrial customers is proposed. Different demand side management (DSM) techniques suitable for distinct week-ahead and day-ahead timescales cooperate for achieving the overall optimal demand scheduling in the entire multi-timescale frame. Specifically, in the week-ahead scheduling, a dynamic scenario generation method is proposed to accurately simulate uncertainties of customer electricity demand time-series during the scheduling horizon, which can represent not only the marginal distribution of possible customer loads at each time instant but also the joint distribution among multiple loads at different time instants. In addition, priorities of various DSM techniques accepted by DSM participants and their willingness are also considered, aiming at mitigating impacts on their normal manufacturing process. With actual historical load data of industrial customers from advanced metering infrastructure system, the dynamic scenario generation method is shown to be effective in preserving statistic features of load fluctuations, and the proposed optimal multi-timescale coordinated demand side scheduling model is demonstrated to be an effective DSM approach.

New real‐time demand‐side management approach for energy management systems

This study proposes a new demand-side management (DSM) technique, which is characterised by low computational requirements. The proposed technique relies on developing an operational matrix by the device local controller based on the device characteristics and the customer preferences. This matrix is sent to the energy management system (EMS) without the need to send any further information about the device or the customer preferences; then, the EMS chooses the optimal schedule for the device. To demonstrate the effectiveness of the proposed DSM technique, it is incorporated in an EMS that consists of three units controlled by a centralised microgrid controller (MGC). The three units managed by the MGC are the data collection and storage engine, the forecasting engine, and the optimisation engine. The EMS utilises the rolling horizon concept to manage real-time information and to provide the plug-and-play option for all controllable devices. Simulation results on a typical microgrid system show that the proposed DSM technique outperforms conventional DSM approaches in terms of the computational time.

Multi-objective home energy management with battery energy storage systems

Home energy management (HEM) is one of the crucial requirements for future smart grid environment. Usually, utility companies motivate residential consumers to participate in household demand side management (DSM) techniques. The residential consumers’ main intention is to reduce their electricity bills whereas utilities’ primary goal is to reduce their system peak load demand. The benefits of DSM can be enhanced with smart energy storage systems. In this paper, an intelligent multi-objective household DSM with battery storage systems is proposed to minimize both the objectives simultaneously. Generally, the residential DSM deals with various controllable appliances that normally have different operating time priorities according to the consumers’ preferences. Because of this, the DSM problem is modelled as a mixed integer optimization problem. In this paper, the multi-objective mixed integer linear programming technique (MOMILP) is used to solve the proposed residential DSM problem. The proposed model is tested on a system having four different residential consumers. The benefits of the battery energy storage systems are analysed by taking six different scenarios. An economic analysis of each residential consumer's battery storage system with load uncertainty modelling is also done in this paper. Simulation results of various scenarios with different objectives demonstrate the effectiveness of the proposed model.