Implementation Of Demand Response Programs Research Articles

Optimal scheduling of heating and power hubs under economic and environment issues in the presence of peak load management

Financial issues have been always one of major priorities in scheduling of energy systems. Although these systems are able to serve several types of energy demands but generated emission by these systems is a challenging problem. Since improvement of each one of mentioned issues has negative effects on the other issue therefore a trade-off solution is necessary to be obtained between these issues. In this paper, a multi-objective model has been presented to satisfy both economic and environmental objectives of a hub energy system in the presence of demand response program. In the proposed paper, ε-constraint and max–min fuzzy satisfying methods have been employed to solve and select the trade-off solution. The main reason of implementation of demand response program is to reduce operation cost and improve environmental performance of hub energy system. In fact, demand response program transfers some percentage of load from peak periods to off-peak periods to flatten load curve which leads to reduction of cost and emission. A mixed-integer linear programming has been used to simulate the proposed model and general algebraic modeling system software has been utilized to solve it. A sample hub energy system containing renewable and non-renewable energy resources has been studied and comparison results are presented to validate efficiency of proposed techniques.

Strategic decision-making of distribution network operator with multi-microgrids considering demand response program

In this paper, the operation scheduling of future distribution network is discussed under assumption of multi-microgrids. The economic operation scheduling of distribution network operator in the presence of autonomous microgrids is formulated as a game problem. Due to participation of multiple players with different objective functions, the multi follower bi-level programming is intended in the energy management scheme. From the distribution network operator's perspective, the decision-making strategy seeks to minimize the operation costs of distribution network operator. Distribution network operator interconnecting with upstream grid and interaction with microgrids, schedules distributed energy resources and loads in economical matter to operate in optimal operation point. On the other hand, microgrid owners fed electrical loads interacting with the other microgrids and distribution network operator implementing demand response program during the scheduling horizon to acquire maximum profit. Therefore, the key focus of this approach is decision-making of distribution network operator which will be affected, not only by the reactions of microgrids, but also by the exchanging power between the microgrids. The paper discusses the theoretical background, architecture and algorithms of the proposed multi follower bi-level programming, and demonstrates the performance of distribution network operator and microgrids by means of simulation on the radial distribution test system.

Dynamic Negawatt Demand Response Resource Modeling and Prioritizing in Power Markets

In recent years, integrated use of demand- and supply-side resources has been performed by electric utilities, because of its potential attractiveness, both at operation and economic levels. Demand Response Resources (DRRs) can be used as demand side options which are the consequence of implementing Demand Response Programs (DRPs). DRPs comprise the actions taken by end-use customers to reduce their electricity consumption in response to electricity market’s high prices; and/or reliability problems on the electricity network. In this paper, a dynamic economic model of DRPs is derived based upon the concept of flexible elasticity of demand and the customer benefit function. Precise modeling of these virtual negawatt resources helps system operators to investigate the impact of responsive loads on power system studies. This paper also aims to prioritize multifarious DRPs by means of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and entropy methods. Performance of the proposed model is investigated through numerical studies using a standard IEEE test system.

Suitability of Demand Response Program Equipped with Solar Energy in UTHM

Demand Response Program (DRP) is a program developed in western country to cater high load demand. This system enhance the quality of the current smart grid system. This program encourage two-ways interaction between the consumer and the utility provider. The consumers are required to reduce their load consumption upon request by the utility provider, thus avoiding them from paying for the high load demand. However, the current design of DRP is seem to put the user at disadvantages. Thus, deployment of the new practice equipped with renewable energy sources will make the system more user- friendly. As DRP prove to be beneficial in many terms, this project is conducted to find DRP relevancy to be implemented in Universiti Tun Hussein Onn Malaysia (UTHM). According to the electrical consumption data disclosed by Facility Management Division, the power consumed by UTHM is remarkably high especially during 11.00 am to 12.00 pm. Thus, DRP is being proposed to be put into practise during this period. Nonetheless, it is not economically wise to implement the program to the whole campus. Therefore, three buildings with the most power consumption had been chosen for this study. They are the library, Faculty of Civil and Environmental Engineering (FKAAS) building and Faculty of Technology and Vocational Education (FPTV) building. The building power consumption and economic evaluation had been analysed using HOMER simulation. From the analysis, the combination of FPTV and FKAAS had been chosen for DRP implementation. The reason are, these buildings have superiority over the library in term of reliability and effectiveness. In terms of economy, it is almost equivalent to each other.

Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings

Demand Response (DR) aims at improving the operation efficiency of power plants and grids, and it constitutes an effective means of reducing grid risk during peak periods to ensure the safety of power supplies. One key challenge related to DR is the calculation of load baselines. A fair and accurate baseline serves as useful information for resource planners and system operators who wish to implement DR programs. In the meantime, baseline calculation cannot be too complex, and in most cases, only weather data input is permitted. Inspired by the strong non-linear capabilities of Support Vector Regression (SVR), this paper proposes a new SVR forecasting model with the ambient temperature of two hours before DR event as input variables. We use electricity loads for four typical office buildings as sample data to test the method. After analyzing the model prediction results, we find that the SVR model offers a higher degree of prediction accuracy and stability in short-term load forecasting compared to the other seven traditional forecasting models.

Demand side management in a smart micro-grid in the presence of renewable generation and demand response

In this study, a stochastic programming model is proposed to optimize the performance of a smart micro-grid in a short term to minimize operating costs and emissions with renewable sources. In order to achieve an accurate model, the use of a probability density function to predict the wind speed and solar irradiance is proposed. On the other hand, in order to resolve the power produced from the wind and the solar renewable uncertainty of sources, the use of demand response programs with the participation of residential, commercial and industrial consumers is proposed. In this paper, we recommend the use of incentive-based payments as price offer packages in order to implement demand response programs. Results of the simulation are considered in three different cases for the optimization of operational costs and emissions with/without the involvement of demand response. The multi-objective particle swarm optimization method is utilized to solve this problem. In order to validate the proposed model, it is employed on a sample smart micro-grid, and the obtained numerical results clearly indicate the impact of demand side management on reducing the effect of uncertainty induced by the predicted power generation using wind turbines and solar cells.

The values of market-based demand response on improving power system reliability under extreme circumstances

Power system reliability faces serious challenges when supply shortage occurs because of unexpected generation or transmission line outages especially during extreme weather conditions. Alternative to conventional approaches that solicit aids from the generation side, operators can now leverage the demand-side resources through a variety of electricity market mechanisms to balance the active power and enhance system reliability. The benefits of the demand response (DR) have long been recognized in many works and empirical cases. Systematic analyses, however, have never been addressed to assess the values of the market-based DR for supporting system reliability. In this paper, a case study on the performance of DR in PJM Interconnections during the 2014 North American Polar Vortex is provided to highlight the significant contributions to improving system reliability and maintaining grid stability from DR programs. The unique merits in technical, economic and environmental aspects exhibited by DR during this extreme event verse conventional system-reliability-improving approaches are also demonstrated accordingly. Furthermore, we reveal the difference of DR programs driven by various existing market mechanisms after describing the fundamental DR functions. Values of various DR programs are also highlighted. At last, challenges and opportunities facing China on the design and implementation of DR programs during the transform from the monopoly scheme to an open electricity market during the power industry restructuring in recent years are also discussed.

Modeling and simulation of wind turbines and responsive loads in smart grids

In order to implement demand response programs appropriate equipment must be provided to subscribers at any moment be aware of the price of electricity and the price of electricity at different times to an appropriate response to reduce their costs to the network, that This would reduce consumers will demand at peak times. Smart grid using two-way communication and transmission of information to consumers as well as the use of advanced measurement system, reasonable structure has provided for the full implementation of demand response programs. In this paper, a wind turbine that is connected to a smart grid responsive loads to consider and examine the systems in the grid.

Selling price determination by electricity retailer in the smart grid under demand side management in the presence of the electrolyser and fuel cell as hydrogen storage system

In this research, an approach for selling price determination by electricity retailer in smart grid has been proposed with considering uncertainty. Also, hydrogen storage system (HSS) containing electrolyser (EL), hydrogen storage tanks (HST) and fuel cell (FC) are used as energy storage system (ESS) in smart grid. Also, demand response program as virtual generation units is proposed to increase retailer profit. Furthermore, renewable energy sources, i.e., photovoltaic (PV) system and wind turbine (WT), as well as power market (PM), bilateral contracts (BCs) and distributed generation (DG) units are used as multiple energy procurement sources. All uncertain parameters are modelled using the scenario-based stochastic framework. In the proposed model, the selling price is determined based on real-time pricing (RTP) by the retailer and it is compared with time-of-use pricing (TOU) in the smart grid. The selling price determination based on RTP and demand response program implementation increases the expected profit of retailer. Four case studies are investigated to validate the proposed model.

Dynamic Economic Dispatch Problem Integrated With Demand Response (DEDDR) Considering Non-Linear Responsive Load Models

Intelligent implementation of demand response programs (DRPs) not only decreases electricity price in electricity markets, but also improves network reliability. In this paper, the dynamic economic dispatch (DED) problem has been optimally integrated with the incentive-based DRPs. Moreover, mathematical load modeling can be so effective in the load curve estimation with the lowest error. So, economic models of the linear and non-linear responsive loads (power, exponential, and logarithmic) have been developed for time-based and incentive-based DRPs and integrated with DED. Also, a procedure to select the most conservative responsive load model for the load estimation has been presented too. Also, determining the optimal incentive in the incentive-based DRPs is one of the independent system operator’s challenges. In the proposed combined model, the fuel cost is minimized and the optimal incentive is determined simultaneously. Valve-point loading effect, prohibited operating zones, spinning reserve requirements, and the other non-linear practical constraints make the combined problem into a complicated, non-linear, non-smooth, and non-convex optimization problem, which has been solved with a population-based meta-heuristic algorithm namely random drift particle swarm optimization algorithm. The proposed combined model is applied on a ten units test system. Results indicate the practical benefits of the proposed model.

Customer baseline load models for residential sector in a smart-grid environment

Abstract Demand response (DR) can expand the customer participation in the electricity market and lead by changing its pattern from a simple function to an interactive relation. There are various methods to evaluate the successful implementation of DR program, the most important of which is determination of customer baseline load (CBL). In fact, CBL is the expected pattern of customer consumption in the absence of DR programs. Few works have been done in the field of calculation of CBL in residential sector, while most of them have paid little attention to the impact of changes in weather conditions on these calculations. In this paper, a new method is presented for the calculation of CBL for customers in residential sector in the context of a smart grid, considering the impact of weather changes. The results clearly show the high impact of changes in weather conditions on the calculation of CBL, and also show the extent of effect of buildings’ improved insulation on this parameter. It is also indicated that implementing DR programs can increase the willingness of customers in residential sector to improve the insulations of their buildings.

Day-ahead resource scheduling of a renewable energy based virtual power plant

The evolution of energy markets is accelerating in the direction of a greater reliance upon distributed energy resources (DERs). To manage this increasing two-way complexity, virtual power plants (VPPs) are being deployed today all over the world. In this paper, a probabilistic model for optimal day ahead scheduling of electrical and thermal energy resources in a VPP is proposed where participation of energy storage systems and demand response programs (DRPs) are also taken into account. In the proposed model, energy and reserve is simultaneously scheduled considering the uncertainties of market prices, electrical demand and intermittent renewable power generation. The Point Estimate Method (PEM) is applied in order to model the uncertainties of VPP’s scheduling problem. Moreover, the optimal reserve scheduling of VPP is presented which efficiently decreases VPP’s risk facing the unexpected fluctuations of uncertain parameters at the power delivery time. The results demonstrated that implementation of demand response programs (DRPs) would decrease total operation costs of VPP as well as its dependency on the upstream network.

Determining Optimal Buses for Implementing Demand Response as an Effective Congestion Management Method

Demand response (DR) can be counted as an effective congestion management method in power systems. However, appointing the optimal buses for the demand response programs (DRPs) implementation is one of the main challenges for the power system operators. In this paper, a new procedure has been developed by which the optimal locations and times of DRPs implementation are determined. Optimal buses are identified based on the power transfer distribution factors (PTDFs), available transfer capability, and dynamic dc optimal power flow problem. Applying the developed method results in reducing lines' congestion, increasing customers, and independent system operator's benefits, improving load curve's characteristics, preventing line outages and black outs, and consequently increasing the network reliability. The proposed method has been applied on the IEEE 39-bus New England test system. Results indicate the effectiveness and practical benefits of the proposed method.

Demand Response Programs Design and Use Considering Intensive Penetration of Distributed Generation

Further improvements in demand response programs implementation are needed in order to take full advantage of this resource, namely for the participation in energy and reserve market products, requiring adequate aggregation and remuneration of small size resources. The present paper focuses on SPIDER, a demand response simulation that has been improved in order to simulate demand response, including realistic power system simulation. For illustration of the simulator’s capabilities, the present paper is proposes a methodology focusing on the aggregation of consumers and generators, providing adequate tolls for the demand response program’s adoption by evolved players. The methodology proposed in the present paper focuses on a Virtual Power Player that manages and aggregates the available demand response and distributed generation resources in order to satisfy the required electrical energy demand and reserve. The aggregation of resources is addressed by the use of clustering algorithms, and operation costs for the VPP are minimized. The presented case study is based on a set of 32 consumers and 66 distributed generation units, running on 180 distinct operation scenarios.

The Consumer Rationality Assumption in Incentive Based Demand Response Program via Reduction Bidding

Because of the burgeoning demand of the energy, the countries are finding sustainable solutions for these emerging challenges. Demand Side Management is playing a significant role in managing the demand with an aim to support the electrical grid during the peak hours. However, advancement in controls and communication technologies, the aggregators are appearing as a third party entity in implementing demand response program. In this paper, a detailed mathematical framework is discussed in which the aggregator acts as an energy service provider between the utility and the consumers, and facilitate the consumers to actively participate in demand side management by introducing the new concept of demand reduction bidding (DRB) under constrained direct load control. Paper also presented an algorithm for the proposed framework and demonstrated the efficacy of the algorithm by considering few case studies and concluded with simulation results and discussions.

Risk-based bidding of large electric utilities using Information Gap Decision Theory considering demand response

The present study presents a new risk-constrained bidding strategy formulation of large electric utilities in, presence of demand response programs. The considered electric utility consists of generation facilities, along with a retailer part, which is responsible for supplying associated demands. The total profit of utility comes from participating in day-ahead energy markets and selling energy to corresponding consumers via retailer part. Different uncertainties, such as market price, affect the profit of the utility. Therefore, here, attempts are made to make use of Information Gap Decision Theory (IGDT) to obtain a robust scheduling method against the unfavorable deviations of the market prices. Implementing demand response programs sounds attractive for the consumers through providing some incentives in one hand, and it improves the risk hedging capability of the utility on the other hand. The proposed method is applied to a test system and effect of demand response programs is investigated on the total profit of the utility.

Integrating commercial demand response resources with unit commitment

This paper investigates the impact of demand response resources (DRRs) as the consequence of implementing demand response programs (DRPs) on power markets. Indeed, this paper incorporates commercial concept of DRPs with unit commitment (UC) to solve “unit and DR commitment” problem. This mixed problem will decrease the network operation cost by using of DRPs’ potential to mitigate some UC constraints and avoiding some highly priced generation of units. Here, employing the proposed DRPs model is considered as a new concept in electricity market. In this paper, a dynamic approach is proposed for participating DR service providers in power markets in order to maximize their profits. This paper also aims to concurrently consider the aforementioned commercial DRPs supply model with the generators supply curves in the unit commitment problem, which is solved to minimize operational costs considering multifarious constraints. Performance of the proposed approach is investigated through numerical studies using a standard IEEE 10-unit test system. The results show the efficiency and advantage of the proposed methodology.

An investigation on the impacts of regulatory support schemes on distributed energy resource expansion planning

This paper proposes a novel approach for distributed energy resource (DER) expansion planning from investors' viewpoint based on a combination of dynamic programming algorithm and game theory. An innovative framework is proposed in this paper to consider different aspects of distributed energy resource planning. Wind turbines, gas engines and demand response (DR) programs are considered as DERs in this study. The intermittent nature and uncertainty of wind power generation and also uncertainty of demand response programs will cause the investors to consider risk in their investment decisions. In order to overcome this problem, a modified model has been derived to study the regulatory intervention impacts on wind expansion planning and implementing DR programs. Support schemes were contributed to both wind and DR programs to determine an optimum penetration level of these sources in distribution power system. A model based on intermittent nature of wind power generation and uncertainties of DR programs is developed which can calculate optimal investment strategies. The effectiveness of the proposed model is proved through implementing on a test system.

The Review of Demand Response Programs in Smart Grid

Demand response (DR) has become an important part of smart grid, mainly for modulating load curve, emergency treatment, balancing the demand and supply of power system. The features of smart grid-self healing and interactive-give the new meaning to DR. In smart grid, the promotion strategies of ISO/RTO in energy and auxiliary service markets implement DR programs in the project of auxiliary services and can reduce energy consumption. This paper summarizes the global related researches and practices, including the classification, progress of each subclass, the future development potential and DR program outlook in China, and puts forward the following two ideas: 1) The DR for single market cannot satisfy the needs of smart grid, advanced measurement and remote sensing technologies shall be made to implement the DR programs combining with multiple markets; 2) The service agency of DR on transmission and distribution shall be conducted and supervised by the government departments.

Flexible demand response programs modeling in competitive electricity markets

In recent years, extensive researches have been conducted on implementation of demand response programs (DRPs), aimed to electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market liquidity. Basically, DRPs are divided into two main categories namely, incentive-based programs (IBPs) and time-based rate programs (TBRPs). Mathematical modeling of these programs helps regulators and market policy makers to evaluate the impact of price responsive loads on the market and system operational conditions. In this paper, an economic model of price/incentive responsive loads is derived based on the concept of flexible price elasticity of demand and customer benefit function. The mathematical model for flexible price elasticity of demand is presented to calculate each of the demand response (DR) program’s elasticity based on the electricity price before and after implementing DRPs. In the proposed model, a demand ratio parameter has been introduced to determine the appropriate values of incentive and penalty in IBPs according to the level of demand. Furthermore, the importance of determining optimum participation level of customers in different DRPs has been investigated. The proposed model together with the strategy success index (SSI) has been applied to provide an opportunity for major players of the market, i.e. independent system operator (ISO), utilities and customers to select their favorite programs that satisfy their desires. In order to evaluate the performance of the proposed model, numerical studies are conducted on the Iranian interconnected network load profile on the annual peak day of the year 2007.