جستجوی مقالات مرتبط با کلیدواژه « demand response » در نشریات گروه « برق »

One of the achievements of the restructuring of the electricity industry is the creation of a competitive environment in the electricity generation sector, in which a set of small-scale generation units with loads and a covered network, which managed by a specific institution and is called a virtual power plant, can Have an active presence in the wholesale market of energy and rotating storage. This study presents a new framework for planning the optimal bidding strategy for the participation of virtual power plants in the energy and reserve markets, in which wholesale electricity prices, retail market, required storage, and period are the main parameters. A genetic algorithm is used to solve the resulting optimization problem. Uncertainties governing wholesale prices and forecasting consumption needs in the area covered by the virtual power plant are considered. The logarithmic and normal probability distribution functions were used, respectively. The method of Monte Carlo simulation is used to considering uncertain parameters. The simulation results of this paper show that the proposed method is a powerful and appropriate tool to develop an optimal strategy for virtual power plant bidding in the electricity market, taking into account uncertainties and its proper interaction with consumers with consideration of demand response.

In traditional generation systems, about 25% of energy is wasted, and the presence of distributed energy resources (DER) such as photovoltaic, wind turbines, fuel cell, and the combined heat and power can reduce fuel consumption, pollution, and transmission losses. In this paper, a complete energy management framework in a microgrid is proposed by considering the constraints using Improved Shuffled Frog Leaping Algorithm, in which the exact share of energy production for different units is determined. The proposed scheme is used to select the best arrangement of DERs in the microgrid, the output of which is to determine the number and optimal location of DERs in several bus-bars. Then, the independent system operator determines the quantity of energy exchange and consumption by considering load distribution constraints. Boilers and CHPs have also been used to maintain the balance between the production of thermal power by energy sources and thermal demands. In addition, the Demand Response Program has been used with the aim of smoothing the load curve and of reducing the operating costs. Finally, the proposed method has been implemented and simulated using MATLAB software on a two standards 69-and- 118-bus IEEE system. The comparison of the results with other algorithms showed the accuracy and superiority of the proposed method from the point of view of reducing operating costs.

Today, electricity service providers have to consider economic index as well as social indicators and consumer perspectives. In this paper, by studying the consumer satisfaction function, a new load model based on nonlinear consumer perspective has been developed. The main variable in the proposed model is the coefficient of individual consumer behavior, which requires field and statistical activities to estimate. As an alternative solution in this paper, the relationship between demand elasticity and individual behavior coefficients has been mathematically developed to estimate these coefficients through demand elasticity information. Also, a new demand response program based on individual behavior and satisfaction function has been developed to determine the incentive rate for consumer participation in an optimal way based on their individual perspective. In the numerical results section, the data of Iran's electricity network has been selected for study. Numerical results showed that in order to study the annual consumption change, in addition to increasing prices, inflation rates and increasing consumer incomes also need to be examined. It was also shown that for the Iranian electricity grid, the demand response program implementation for the household consumers has the lowest cost. However, in order to achieve maximum welfare, it is more appropriate these programs have been applied to the public customers.

The concept of demand response (DR) continues to evolve, and its various capabilities are being investigated to enhance the efficiency of nowadays electric power industries. To this end, the barriers that limit DR capabilities should be resolved. This paper provides a new efficient decision model for energy service providers in smart distribution networks to make the maximum use of DR potential as the most cost-effective solution. The correct and proper application of the DR problem provides special capabilities for these entities and can lead to more profit. On the other hand, participating in the upstream market and demand allocation in the downstream network are two main tasks of energy providers. These two tasks affect each other, and simultaneous attention to them is needed for more efficiency. Generally, conservative participation in the upstream market is the main problem of these entities due to the uncertainty of load forecasting, especially considering that the DR problem will aggravate this uncertainty. In these conditions, the interactions between the load curve and price changes should also be considered. To better understand, suppose that an energy provider wants to reduce its energy purchase cost by applying DR. This entity initially forecasts its load consumption and participates in the electricity market. After market clearing, the values of locational marginal price (LMP) are determined for the next 24 hours. Now, applying DR and moving the load consumption to the less expensive hours will reduce the final purchase cost. However, moving the load consumption leads to changes in the LMP values in the substation bus of the distribution network. It is due to the dependencies between the load consumption and the prices. Disregarding these dependencies will limit DR capabilities. Therefore, a new two-step sequential framework is proposed in this paper to enhance the performance of the energy providers in the smart distribution network. The main problem is the optimization of the power purchase cost for the downstream network using DR. The subsidiary problem includes electricity market modeling. The load curve is determined in the main problem, and the amounts of the energy price under different conditions are determined in the subsidiary problem recursively. This framework guides the energy provider to analyze how market clearing affects DR and vice versa. To model load flexibility, a residential distribution network with different types of responsive appliances is utilized, and the model is studied using two case studies. The results demonstrate that applying the proposed framework leads to more reliable and optimal results and has significant benefits for the strategic performance of energy service providers.

Under the smart power systems, determining the amount of Demand Response Resources(DRRs) potential is considered as a crucial issue due to affecting in all energy policy decisions. In this paper, the potential of DRRs in presence of cooling and heating equipment are identified using k-means clustering algorithm as a data mining technique. In this regard, the energy consumption dataset are categorized in different clusters by k-means algorithm based upon variations of energy price and ambient temperature during peak hours of hot (Spring and Summer) and cold (Autumn and Winter) periods. Then, the clusters with the possibility of cooling and heating equipment’s commitment are selected. After that, the confidence interval diagram of energy consumption in elected clusters is provided based upon energy price variations. The nominal potential of DRRs, i.e. flexible load, will be obtained regarding the maximum and minimum differences between the average of energy consumption in upper and middle thresholds of the confidence interval diagram. The energy consumption, ambient temperature and energy price related to BOSTON electricity network over a six-year horizon time is utilized to evaluate the proposed model.

In imprecision-tolerant applications such as image processing and machine learning, imprecision can be tolerated because of the nature of the application itself or the limitation of human senses. By using the approximate computation in these applications, significant power, delay, or area reductions can be achieved. In this paper, two approximate full adders and an approximate adder, with low error are proposed. The effects of die-to-die (D2D) process variation on the threshold voltage of approximate circuits have been evaluated. For evaluating the accuracy and the variability, these approximate full adders have been used and analyzed in the ripple carry adder structure, image Sharpening and image Smoothing algorithms. In terms of power-delay-product (PDP), accuracy, and area for uniformly distributed inputs, the proposed approximate full adder 1, exhibits the best performance, and the proposed approximate full adder 2 and the proposed approximate adder, show the best peak-signal-to-noise ratio (PSNR) for real images.

Integrating renewable resources to provide local load has created a concept called microgrid. With the widespread introduction of microgrids, energy management and system utilization and resources in the electricity market are important tasks of microgrid management. In this paper, the problem of microgrid utilization is modeled taking into account economic, technical and uncertainties related to power consumption, wind speed and solar radiation in electricity market conditions. One of the most important issues in the electricity market is the discussion of the participation of units in real price conditions. In this paper, a framework for the exploitation of electricity and the consumption of controllable loads through integrated utilization of distributed energy sources of uncertainty is presented from a consumer perspective. The optimization problem is a two-step stochastic linear programming that minimized the cost of microgrid operation and expected cost of consumers considering the consumer’s requirement for controllable loads in the desire time interval and distribution company constraints that solved by using Salp swarm optimization algorithm. RBT and IBR tariffs are employed for modeling retail power market for better reflection of wholesale price volatility and avoid of the concurrent use of consumers. In this method price announced to the consumers by retailers only is limited specific later hours instead of the entire operation period. In this condition any timing of controllable loads need to price forecasting, while this forecasting have some uncertainties. These uncertainties are modeled using Monte Carlo method for stochastic price variable scenario generation. MATLAB software is employed for simulation and verification of the proposed method.

In developed societies, residential customers use high-level appliances. The progress in the smart grids and the internet of things have eased the way for home energy management to schedule controllable appliances. Looking to demand increment, demand response strategies aiming at energy management, to achieve goals such as demand reduction and improving reliability, has received attention. A deep review of the existing literature shows the notable efforts put into optimizing the home energy management problem through classic and meta-heuristic optimization algorithms such as game theory, genetic algorithm, and PSO. But, it is worth saying that these algorithms are not pragmatic due to the inherent nature of the home energy management problem. To be more precise, as the environment of the problem changes continuously, these algorithms fail to solve the problem. Hence, some essential assumptions such as considering fixed scenarios are presumed in previous works to enable the conventional algorithm to solve the problem. This is while machine learning addresses this issue by extracting the main features from input data and constructing a general description of the environment. Implementation of machine learning-based algorithms to a home energy management problem requires smart appliances. Hence, in the case of having a smart home, taking the advantage of artificial intelligence for energy management would be feasible and useful. It should be noted that electricity cost reduction can make the demand response program inviting, where customer satisfaction is taken into consideration. Accordingly, customer satisfaction should be considered in the problem formulation. Regarding the mentioned issues, lately, with the remarkable progress in machine learning, novel algorithms evolved for solving optimal decision-making problems such as demand response. Machine learning can be categorized into three main categories, namely supervised learning, unsupervised learning, and reinforcement learning (RL). Among them, reinforcement learning has shown notable performance in decision-making problems. Q-Learning is a model-free RL algorithm that solves nonlinear problems through estimating and maximizing the cumulative reward, triggered by decided actions. The fundamental idea of this algorithm is to identify the best action in each situation. This paper aims to provide a day-ahead demand response program for a smart home. It is done by specifying the quantity of the energy consumption of each appliance, aiming to reduce the electricity cost and user dissatisfaction. In this respect, it is presumed that the smart home is equipped with smart appliances. Moreover, smart meters are installed on appliances to monitor the statuses and receive the command signals from the devices at each hour. These appliances can be divided into three categories, non-responsive, time-shiftable, and controllable loads. Dishwasher and washing machine as time-shiftable loads, EV, air conditioner, and lighting system as controllable loads, and TV and refrigerator as non-responsive loads are taken into account. All in all, we recommend an advanced home energy management system proposing the following contributions: i) Proposing a day-ahead multi-agent Q-Learning method to minimize the electricity cost. ii) Proposing a satisfaction-based framework, which employs a precise model of the customer dissatisfaction functions (i.e., thermal comfort, battery degradation, and desirable operation period). 

In this paper, a multi-agent Q-Learning approach is used to solve the home energy management for a smart home. Q-learning is a popular model-free algorithm among reinforcement learning algorithms, due to the fact that its convergence is proven, and it is feasible to implement, as well. In order to deploy Q-Learning on a home energy management system, first of all, smart home should be formed as a Markov decision process. A Markov decision process consists of four fundamental parameters namely, state, action, reward, and transition probability matrix. Afterward, an agent is trained through experiencing a specific state, taking an action, transition to a new state, and calculating the cumulative reward. By doing so, after visiting a considerable number of states and taking diverse decisions, it will learn gradually to select the optimum action whatever the state is.Another fundamental aspect of this paper is the proposed approach to take customer satisfaction into account. In this paper, a non-linear thermal comfort model, non-linear desirable operation period model, and linear battery degradation model are deployed to consider the customer dissatisfaction, precisely. It should be noted that all simulations have been implemented by python 3.6 programming language without making use of any commercial solver. 

Various case studies have been designed to verify the effectiveness of the proposed method. Scenario 1 is designed to simulate the behavior of a smart home associated with a random manner of energy usage. Scenario 2 is designed to verify the effectiveness of the proposed home energy management system, where Q-Learning is conducted. In this case, battery degradation is overlooked. Scenario 3 is similar to the previous one, where battery degradation is also taken into consideration. Comparing the obtained results indicates that the proposed algorithm has successfully reduced the electricity bill by 31.3% and 24.8% in scenarios 2 and 3, respectively. It is worth saying that customer satisfaction is not violated in mentioned scenarios. Furthermore, in order to evaluate the effect of thermal comfort on the electricity bill, another case study is deployed, where the thermal comfort coefficient is decreased to smaller magnitudes. As expected, the less thermal comfort coefficient, the less electricity bill. The reason behind this is that having a lower thermal comfort coefficient leads to less importance of temperature control compared to the electricity bill.

This paper proposed a method for home energy management, regarding minimizing the electricity bill and user discomfort. In this paper, a multi-agent reinforcement learning via Q-Learning is used to make optimal decisions for home appliances, which are categorized into non-shiftable loads, time-shiftable loads, and controllable loads. Comparing to classic optimization methods, the proposed approach in this paper is capable of modeling more appliances and solving complex problems, due to the inherent nature of the Q-Learning algorithm. Implementing the proposed method in the numerical study section led to a 24.8% electricity bill reduction. The numerical results prove the effectiveness of the proposed approach.

In this paper, a two-stage stochastic programming model based on a multi-objective optimization has been proposed for optimal operation of smart micro-grid (MG) aiming at minimizing operational costs and environmental emissions in presence of renewable resources and demand response. In the presented model, the forecasting error of the renewable resources productions is modeled by probability density functions and demand response has been implemented to cover the uncertainty of the renewable resources. Here, it is assumed that the MG operator decides on two stages for optimum management of its network; first stage is the operation in the base state and the second one is pertaining to the domains of different scenarios for generation of renewable resources. The base state of the micro-grid refers to the situation in which the active power productions of renewables are equal to the predicted values. To solve the problem, Multi-Objective Particle Swarm Optimization method has been used and TOPSIS technique has been applied to extract the output from the Pareto Frontier. The proposed approach is applied to a typical MG and the numerical results show the efficiency of demand side management in reducing costs and environmental emissions as well as covering the uncertainty resulting from renewables.