Effect Of Demand Side Management Research Articles

Farm electricity system simulator (FESS): A platform for simulating electricity utilisation on dairy farms

The objective of this paper was to define, validate and demonstrate a model capable of accurately simulating dairy farm electricity consumption across varying herd and parlour sizes, to facilitate research investigating renewable energy systems (RES) and demand side management (DSM). The Farm Electricity System Simulator (FESS) was developed using grey-box modelling techniques utilizing empirical data for parameter tuning. Empirical data were gathered from nine spring calving, pasture based dairy farms located in the Republic of Ireland. A k-means clustering analysis was conducted, separating the farms into three, near homogenous groups, from which representative farms were selected. FESS was trained using 12 months of data from three representative farms using the repeat hold out method for data partitioning with 75 % of data used for training and 25 % used for validation. An optimisation algorithm was used to minimize the error during model training. Through cross-validation, FESS achieved a root mean squared error (RMSE) of 7.65 kWh, mean absolute percentage error (MAPE) of 7.10 %, mean percentage error (MPE) of −0.86 % and a relative prediction error (RPE) of 7.56 % for total daily electricity consumption. Across the three farms, the simulated outputs of FESS achieved an average R2 value of 0.72, demonstrating good agreement with observed data. FESS’s utility was demonstrated by analysing the effects of different electricity pricing structures and on-site solar photovoltaic electricity generation on total farm energy costs. We concluded that FESS simulated on-farm electricity consumption with sufficient accuracy for the intended application. FESS accurately simulated dairy farm electricity consumption across three dairy farms of different herd and parlour sizes while evaluating the effects of demand side management and renewable generation on farm electricity consumption and costs.

Energy Hub Planning and Operation Optimization by Applying Demand Side Management Using GAMS

Energy hub planning and optimization considering various constraints is an emerging domain of research due to the scarcity of conventional energy resources. Strategic integration of multi-energy sources is a dominant region of research as it provides better efficiency and cost savings. Various uncertainties due to the changing electric, heating, and cooling load demand are increasing the complexity of the system. The paper investigates the effectiveness of Demand Side Management (DSM) programs by analyzing the operation of domestic Energy Hub. The paper proposes two stages for optimal energy hub planning and operation optimization and GAMS mathematical modeling is used to deduce the results. The paper provides the analysis of a two-stage optimization model using the approach of modeling uncertainties and constraints. The process of energy hub designing has been carried out by the help of PSO algorithm by considering continuous capacities of various components of energy hub. The research regarding optimal energy hub operation is modelled in GAMS and the approach of mixed-integer non-linear programming (MINLP) has been used to evaluate the results. The framework provides the energy hub model and also provided the verification of model effectiveness and efficiency.

Employing Several Kinds of Demand Response Programs to Optimal Eco Emission Planning of Microgrid

Numerous investigations have been carried out to evaluate microgrid optimal configuration. The main goal of this research is evaluating the positive effect of private investor that contributes in the MG configuration in the planning horizon. The MG operator and private investors are the owners of combined heat and power and intelligent parking lot, respectively. The optimization problem was modeled as a two-objective function. For comprehensive evaluation of the planning problem, reliability indices are considered as problem constraints. To show the effects of demand side management in reducing operational costs, several types of demand response programs are used. In the first stage, a bi-objective function is converted to a single objective function by utilizing the weighted sum technique and in the second stage, the best solution is chosen among all Pareto solutions employing max–min fuzzy decision-making method. To reduce problem complexity, SCENRED tools of GAMS software are used to reduce the number of scenarios. The results revealed that in TOU-based DRP, the total cost of MGO and private investor are reduced by 4.28%, 2.67%, respectively.

Gas Supply Reliability Analysis of a Natural Gas Pipeline System Considering the Effects of Demand Side Management

Abstract At present, China has a developing natural gas market, and ensuring the security of gas supply is an issue of high concern. Gas supply reliability, the natural gas pipeline system's ability to satisfy the market demand, is determined by both supply side and demand side and is usually adopted by the researches to measure the security of gas supply. In the previous study, the demand side is usually simplified by using load duration curve (LDC) to describe the demand, which neglects the effect of demand side management. The simplification leads to the inaccurate and unreasonable assessment of the gas supply reliability, especially in high-demand situation. To overcome this deficiency and achieve a more reasonable result of gas supply reliability, this paper extends the previous study on demand side by proposing a novel method of management on natural gas demand side, and the effects of demand side management on gas supply reliability is analyzed. The management includes natural gas prediction models for different types of users, the user classification rule, and the demand adjustment model based on user classification. First, an autoregressive integrated moving average (ARIMA) model and a support vector machine (SVM) model are applied to predict the natural gas demand for different types of users, such as urban gas distributor (including residential customer, commercial customer, small industrial customer), power plant, large industrial customer, and compressed natural gas (CNG) station. Then, the user classification rule is built based on users' attribute and impact of supplied gas's interruption or reduction. Natural gas users are classified into four levels. (1) demand fully satisfied, (2) demand slightly reduced, (3) demand reduced, and (4) demand interrupted. The user classification rule also provides the demand reduction range of different users. Moreover, the optimization model of demand adjustment is built, and the objective of the model is to maximize the amount of gas supplied to each user based on the classification rule. The constraints of the model are determined by the classification rule, including the demand reduction range of different users. Finally, the improved method of gas supply reliability assessment is developed and is applied to the case study of our previous study derived from a realistic natural gas pipeline system operated by PetroChina to analyze the effects of demand side management on natural gas pipeline system's gas supply reliability.

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

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

A Two-Stage Optimal Scheduling Model of Microgrid Based on Chance-Constrained Programming in Spot Markets

Aimed at the coordination control problem of each unit caused by microgrid participation in the spot market and considering the randomness of wind and solar output and the uncertainty of spot market prices, a day-ahead real-time two-stage optimal scheduling model for microgrid was established by using the chance-constrained programming theory. On this basis, an improved particle swarm optimization (PSO) algorithm based on stochastic simulation technology was used to solve the problem and the effect of demand side management and confidence level on scheduling results is discussed. The example results verified the correctness and effectiveness of the proposed model, which can provide a theoretical basis in terms of reasonably coordinating the output of each unit in the microgrid in the spot market.

Demand response events in district heating: Results from field tests in a university building

Demand side management will play a major role in future energy systems. However, while they have been explored in some depth for electricity grids, a similar progress has not been made for district heating networks (DHN). To this end, the current work field-tested the effect of demand side management, in the form of price based, demand response (DR) events, in the DHN catering to a university building. Responding to variations in a pricing model, the temperature of inlet water was varied from the heating water substation. Using combinations of parameters, 11 different DR scenarios were executed. To gauge the effect of the DR interventions, inlet water temperature, room air temperature, and occupant satisfaction were monitored. Depending on the constraints imposed, significant variations in the inlet water temperature and peaks and drops in the room air temperature were noted. The different DR scenarios did not greatly alter occupant satisfaction levels. The study was able to provide useful data from field tests of DR events in a DHN. The data also showed that price based DR events may be triggered and executed without significantly impacting occupant satisfaction with thermal comfort of the premises.

Effects of demand side management on Chinese household electricity consumption: Empirical findings from Chinese household survey

China’s residential electricity demand has grown rapidly over the last three decades and given the expected continued growth, demand side management (DSM) can play an important role in reducing electricity demand. By using micro-level data collected from 1450 households in 27 provinces in the first-ever China Residential Energy Consumption Survey in 2012, this study estimates the effects of three DSM measures empirically: tiered household electricity pricing, China Energy Label program, and information feedback mechanisms. We find these measures have contributed to moderating residential electricity demand growth but additional policy reform and tools are needed to increase their effectiveness and impact. Residential electricity demand is found to be price- and income- inelastic and tiered pricing alone may not be as effective in electricity conservation. The statistically significant relationship between China Energy Label efficient refrigerators - but not televisions - and lowered residential electricity consumption reflect mixed program effectiveness. Lastly, of the information feedback currently available through electricity bills, payment frequency and meters, only meter reader is estimated to be statistically significant. Important policy implications and recommendations for improving each of these three DSM measures to expand their impact on reducing residential electricity consumption are identified.

Investigating the Effect of Demand Side Management on the Power System Reliability

In electric power systems, the generated power should be equal with the demand and power of network is mainly controlled through generation system. In such operation, the demand is satisfied through changing the generated power and afterward, the safe operation of power system is reached. But during recent years, a new concept has been developed in electric power systems namely demand side management (DSM). DSM is the modification of consumer demand for energy through various methods such as financial incentives and education. Usually, the goal of demand side management is to encourage the consumer to use less energy during peak hours, or to move the time of energy use to off-peak times such as nighttime and weekends. One of the important models of DSM is interruptible loads. Interruptible loads are the right for an electricity utility to interrupt supply to a customer, typically during a system emergency, to relieve short term network constraints up to a couple of hours. Interruptible loads can be deployed in one of two ways. The network operator gives notice of an interruptible load event to the customer, then relies on the customer to reduce their electricity usage; or unilaterally interrupts supply to the customer. In this paper, the effect of interruptible loads on the power system reliability is investigated. A multi machine power system is considered as cases study. Simulation results show the great effects of interruptible loads on the power system reliability.

Study the Influence of the Implementation of Demand Side Management on Generation System Reliability

At present, the influence of implementing demand side management on generation system reliability was studied by more and more academics. Demand side management can improve energy efficiency and reduce the maximum load demand, which is equivalent to an indirect increase in power generation capacity of the system, thus can improve the reliability of generation system. This paper firstly introduced the reliability index system of generation system, then the reliability analysis model which includes generation capacity model and load model was respectively built, and then the non-sequential Monte Carlo simulation method applied in generation system reliability evaluation was analyzed. Secondly, adopted the simulation to quantify and set the IEEE reliability testing system RTS (Reliability Test System) as an example to analyze the effect of demand side management on generation system reliability evaluation, the analysis results show that the implementation of DSM can improve the energy utilization and reliability of generation system, thus the system can be more secure, economic and reliable to supply electric power for electricity users.

Smart Grid Implementation of Demand Side Management and Micro-Generation

This paper analyses the influence and effects of demand side management (DSM) and micro-generation (MG) on the operation of future “smart grids.” Using the residential load sector with PV and wind-based MG as an example, the paper introduces a general methodology allowing to identify demand-manageable portion of the load in the aggregate demand, as well as to fully correlate variable power outputs of MG with the changes in load demands, including specific DSM actions and schemes. The presented analysis is illustrated using a detailed model of a typical UK LV/MV residential network.

A Method for Evaluating the General Load and Loss Effects of Demand-Side Management on the T&D Delivery System

A Method for Evaluating the General Load and Loss Effects of Demand-Side Management on the T&D Delivery System

A Method for Evaluating the General Load and Loss Effects of Demand-Side Management on the T&D Delivery System

Historically, the load management, and now demand-side management (DSM), literature has been dominated by system economic and end user analyses of system peaks such as [1], [2], and [3]. Much of this work has not considered the impacts DSM will have on the T&D system loads and losses. Recently, there has been more attention paid to this aspect. Davis, et al [4] quantified the impact on the T&D system with actual distribution impacts, and Willis, et al [5] with detailed modeling. The work performed by the authors was the result of a research project that had the objective to evaluate the impact of certain state-of-the-art and future technologies on improving T&D system efficiency. Load management, which was evaluated as demand-side management in the study, was one of many evaluated. This paper describes the method used to evaluate the impacts of DSM on T&D system loads and losses. The paper also includes a discussion of its application to a sample system.