Demand Response Method Research Articles

Deposit Mechanism Design and Corresponding Decision Strategy considering Uncertainty of Customer Behaviour

Demand response (DR) has received great concern since the significant growth in electricity consumption and peak-valley difference were witnessed recently. Based on the theory of customer psychology, an uncertainty model of customer behaviour is proposed. By converting electric power to deposit points, a novel deposit mechanism is designed in this paper, which can better deal with the special situations in China. Compared with traditional mechanism, the proposed deposit mechanism is capable of improving acceptability of DR methods in China and achieving greater mobilization of customer motivation for its more understandable rules and higher participation compensations. Furthermore, a decision strategy considering benefits of both the power company and the subscribed customers is proposed based on the uncertainty model of customer behaviour and the proposed deposit mechanism, which aims at achieving win-win situations and greater mobilization of customer motivation. The uncertainties in decision strategy are quantified by the uniform design sampling (UDS) method which is more efficient and computationally accurate than traditional Monte Carlo simulation. With the electricity data of Nanjing City, China, the superiority of proposed deposit mechanism and decision strategy are verified by numerical simulations.

Harnessing households to mitigate renewables intermittency in the smart grid

Abstract This paper presents and evaluates a novel demand response method for households, designed for mitigating intermittency in smart grids with a high share of renewables. The method, named Dynamic-Active Demand Response (DADR), is based on an innovative paradigm of offering multiple electricity service levels and a dynamic-active demand response scheme. It provides the grid operator with the ability to influence consumption patterns in real time, so that responding to renewables intermittency is more effective in terms of reliability, predictability and response time. DADR's performance is evaluated using a Monte Carlo simulation model, which assesses the method's intermittency mitigation potential and estimates the expected economic value of implementing it in the Israeli residential sector. Major components of the model are based on a survey, conducted among Israeli households. The survey results indicate that, given a sufficiently attractive economic incentive, participation rates can reach as high as 85–90%. The Monte Carlo simulation results reveal that DADR can make a significant contribution to mitigating renewables intermittency, with energy savings of hundreds to thousands of MWh a day and a positive net economic benefit.

Dynamical demand response method

In this paper authors suggest method of dynamic demand response to consumers of the municipal sector. The electrical grid scheme with adjustments for the application of the dynamic demand response method is described. Results of simulation on Matlab/Simulink are given. Moreover advantages of the met

End‐user participation in a collaborative distributed voltage control and demand response programme

A new generation of electric appliances with controllable reactive power creates an opportunity for operators in distribution systems to be used as a resource for reactive power support. On the other hand, implementing demand response (DR) programmes as an alternative resource to manage the active power demand may also affect the reactive power balance. Collaborative effect of reactive power support devices and the DR programme is investigated in order to control voltage problem in a distribution network. In the first step, distributed voltage (DV) control and DR methods are considered as individual control actions; then, the hybrid DV control with DR (DVDR) method is proposed to improve the voltage profile. The IEEE 33-bus distribution standard test system is chosen for validating the novel method, in which the optimum reactive power injection in the candidate buses and demand curtailment in each area are calculated. The proposed DVDR method can better mitigate the voltage problem and the results showed far desirable performance compared with using just DR or DV methods. The proposed method can also curtail less demand in comparison to the DR method.

A simulation study for residential electricity user behavior under dynamic variable pricing with demand charge

ABSTRACTAttempting to increase energy efficiency and improve system load factors in an electricity distribution system, Demand Response (DR) has long been proposed and implemented as a form of load management. Various pricing structures incentivizing consumers to shift energy consumption from on-peak to off-peak periods are evident in this field. Most DR methods currently used in practice belong to static variable pricing (e.g., Time of Use, Critical Peak Pricing) and the impact of such tariffs has been well established. However, dynamic variable pricing in general is less studied and much less practiced in the field, due to the lack of understanding of consumer behavior in response to price uncertainty. In this article, we study a novel dynamic variable pricing scheme that uses the coincident demand charge to reduce load consumption during peak events. We employ a multi-attribute utility function and model predictive control to simulate consumer behavior of utility maximization in home energy consumption. We use a conditional Markov chain to model and predict the system peak. Effects of the proposed residential electricity rate based on coincident demand charge are compared with other pricing schemes through simulation validated with real-world residential load profiles. Finally, we extend the simulations to study the impact of integrating renewable solar production in a DR program.

A Time-Varying Potential-Based Demand Response Method for Mitigating the Impacts of Wind Power Forecasting Errors

The uncertainty of wind power results in wind power forecasting errors (WPFE) which lead to difficulties in formulating dispatching strategies to maintain the power balance. Demand response (DR) is a promising tool to balance power by alleviating the impact of WPFE. This paper offers a control method of combining DR and automatic generation control (AGC) units to smooth the system’s imbalance, considering the real-time DR potential (DRP) and security constraints. A schematic diagram is proposed from the perspective of a dispatching center that manages smart appliances including air conditioner (AC), water heater (WH), electric vehicle (EV) loads, and AGC units to maximize the wind accommodation. The presented model schedules the AC, WH, and EV loads without compromising the consumers’ comfort preferences. Meanwhile, the ramp constraint of generators and power flow transmission constraint are considered to guarantee the safety and stability of the power system. To demonstrate the performance of the proposed approach, simulations are performed in an IEEE 24-node system. The results indicate that considerable benefits can be realized by coordinating the DR and AGC units to mitigate the WPFE impacts.

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing: An empirical study

With the penetration of smart grid into factories, energy-efficient production scheduling has emerged as a promising method for industrial demand response. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, moderate, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling.

Real-Time Control Based on Multi-Agent Systems for the Operation of Large Ports as Prosumer Microgrids

Full electrification of large ports and intelligent energy networks compose a very promising solution, not only for limiting the in-port greenhouse gas emissions but also for the improvement of their efficiency and electric power system operation support. Large ports comprise a variety of flexible loads like refrigerated containers, electric vehicles, onshore electric power supply to ships at berth, and so on, while they usually have great potential for local energy generation from renewable energy resources, like offshore wind, tides, waves, and so on. Smart grids can be proved very efficient in increasing port power demand flexibility and controllability. The main goal of this paper is to propose an innovative decentralized demand response method that eventually turns a port comprising flexible loads and power generation from renewable energy sources to a prosumer Microgrid. The proposed method is applied to a realistic case study of a large port comprising a large number of flexible loads and one offshore wind park. The efficiency of the proposed method is evaluated by detailed simulations.

Smart grid real‐time pricing optimization management on power consumption of building multi‐type air‐conditioners

Real‐time pricing will be one of the demand response methods for the future smart grid. Assuming an advanced building energy management system for air‐conditioning facilities in commercial buildings, a simulated annealing optimization that minimizes an evaluation function consisting of power cost and comfort degradation terms is studied. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Multi-Period Regional Energy Management Based on Dynamic Pricing with Non-Deficit Real-Time Market Trading

This paper deals with a regional demand response method based on dynamic electricity pricing in a multi-period energy market. In the proposed method, first, the power supply and demand in each area...

Building demand response and control methods for smart grids: A review

Demand response provides a solution to the grid imbalance problems which restrict the use of renewable energy. Since buildings possess a high amount of flexible load, they could contribute to smart grid stability and achieve cost savings for buildings and the other participants in the smart grid. However, the research and application of building demand response are still at the beginning stage. Majority of the existing demand response studies are from the view point of the grid side, rather than the demand side. This article, therefore, provides an overview on the studies on building demand response from the view point of buildings at the demand side. It mainly consists of two parts: (1) an overview of different types of demand response programs, and the status of the demand response programs in several countries and regions; (2) a review on the control methods for demand response in commercial and residential buildings. This review intends to support the further development of demand response methods for future smart grid applications and their implementation in buildings.

Demand Response Program in Smart Grid Using Supply Function Bidding Mechanism

In smart grid, customers have access to the electricity consumption and the price data via smart meters; thus, they are able to participate in the demand response (DR) programs. In this paper, we address the interaction among multiple utility companies and multiple customers in smart grid by modeling the DR problem as two noncooperative games: the supplier and customer side games. In the first game, supply function bidding mechanism is employed to model the utility companies’ profit maximization problem. In the proposed mechanism, the utility companies submit their bids to the data center, where the electricity price is computed and is sent to the customers. In the second game, the price anticipating customers determine optimal shiftable load profile to maximize their daily payoff. The existence and uniqueness of the Nash equilibrium in the mentioned games are studied and a computationally tractable distributed algorithm is designed to determine the equilibrium. Simulation results demonstrate the superior performance of the proposed DR method in increasing the utility companies’ profit and customers’ payoff, as well as in reducing the peak-to-average ratio in the aggregate load demand. Finally, the algorithm performance is compared with a DR method in the literature to demonstrate the similarities and differences.

Smart virtual energy storage control strategy to cope with uncertainties and increase renewable energy penetration

This paper presents novel methods for Demand Response (DR) programs in order to deal with operational uncertainties, such as wind energy and energy price of upstream network, within the framework of a smart Microgrid. Respectively, total loads of a typical Microgrid are classified into three major categories, each of which is represented by a typical load. The First category involves loads with energy storage capability, which is represented by heater loads. In this category, virtual energy storage capability are considered for some special loads. Second group comprises loads with shifting capability that represents by washing machines and the third category consists loads with curtailment capability, which is represented by lighting loads. Using the proposed DR methods, energy consumptions of all mentioned loads are coupled to amounts of wind energy and energy price of upstream network. These methods are applied to the operation of a typical Microgrid, which consists of a dispatchable supplier (microturbine), a non-dispatchable supplier (wind turbine) and an energy storage system. Moreover, the Microgrid has the capability of exchanging energy with upstream distribution network. In order to consider uncertainties, Monte-Carlo simulation method is used, in which various scenarios are generated and applied in the operation of the Microgrid. Finally, simulation results on the Microgrid demonstrate that implementing the proposed DR methods would lead to increasing the total operational profit of the Microgrid from $92 to $108 and also decreasing the risk of low profit.

Reinforcement Learning-Based Plug-in Electric Vehicle Charging with Forecasted Price

This paper proposes a novel demand response method that aims at reducing the long-term cost of charging the battery of an individual plug-in electric vehicle (PEV). The problem is cast as a daily decision-making problem for choosing the amount of energy to be charged in the PEV battery within a day. We model the problem as a Markov decision process (MDP) with unknown transition probabilities. A batch reinforcement-learning (RL) algorithm is proposed for learning an optimum cost-reducing charging policy from a batch of transition samples and making cost-reducing charging decisions in new situations. In order to capture the day-to-day differences of electricity charging costs, the method makes use of actual electricity prices for the current day and predicted electricity prices for the following day. A Bayesian neural network is employed for predicting the electricity prices. For constructing the RL training dataset, we use historical prices. A linear-programming-based method is developed for creating a dataset of optimal charging decisions. Different charging scenarios are simulated for each day of the historical time frame using the set of past electricity prices. Simulation results using real-world pricing data demonstrate cost savings of 10%–50% for the PEV owner when using the proposed charging method.

デマンドレスポンス発動時の配電系統電圧変動問題と蓄電池とSVRの協調電圧制御手法

デマンドレスポンス発動時の配電系統電圧変動問題と蓄電池とSVRの協調電圧制御手法

Electric Springs with Coordinated Battery Management for Reducing Voltage and Frequency Fluctuations in Microgrids

Electric springs based on power electronics have been proposed as a demand response method for stabilizing power grid fed by substantial intermittent renewable energy sources. Associated with energy storage, they can provide both active and reactive power compensation. Due to the limited storage capacity of the battery, this project explores a new control scheme for the third version of the electric springs (ES-3) to operate under the physical constraints of the state-of-charge of the battery for microgrid stability applications. The ES-3 is based on a bi-directional grid-connected power converter with a battery bank. Unlike the traditional control of grid-connected power inverters for injecting renewable power to the power grid, the proposed control scheme puts the stability of the power grid as a high priority while maintaining its normal bi-directional power flow functions. Such a scheme has been tested in an experimental prototype and a power grid simulator. Results are presented in this paper to illustrate the use of the scheme in battery’s monitoring, charging/discharging management, and output power control.

Assessing the benefits of residential demand response in a real time distribution energy market

In the field of electricity distribution networks and with the advent of smart grids and microgrids, the use of Distribution Locational Marginal Price (D-LMPs) in a Real Time (RT) distribution market managed by a Distribution System Operator (DSO) is discussed in presence of empowered residential end-users that are able to bid for energy by a demand aggregator while following Demand Response (DR) initiatives. Each customer is provided by a transactive controller, which reads the locational market signals and answers with a bid taking into account the user preferences about some appliances involved in DR activities and controlled by smart plugs-in. In particular, Heating Ventilation and Air Conditioning (HVAC) appliances and shiftable loads are controlled so that their consumption profile can be modified according to the price of energy.In order to assess the effectiveness of the proposed method in terms of energy and cost saving, an innovative probabilistic methodology for evaluating the impact of residential DR choices considering uncertainties related to load demand, user preferences, environmental conditions, house thermal behavior and wholesale market trends has been proposed. The uncertainties related to the stochastic variations of the variables involved are modeled by using the Monte Carlo Simulation (MCS) method. The combination of MCS and RT distribution market simulation based on D-LMPs are used to assess the operation and impact of the DR method over one month. Simulations results on an 84-buses distribution network confirmed that the proposed method allows saving costs for residential end-users and making the distribution network much reliable against network congestions thanks to the use of D-LMPs.

A Review of Incentive Based Demand Response Methods in Smart Electricity Grids

Smart electricity grid is a complex system being the outcome of the marriage of power systems with computing technologies and information networks. The information transmitted in the network is utilized for controlling the power flow in the electricity distribution grid. Thus smart grid facilitates a demand response approach, where grid participants monitor and respond to information signals with their electricity demand. This review paper focuses on a subclass of demand response methods and more particularly in incentive based demand response. It aims at providing a review of the existing and proposed methods while briefly explaining their main points and outcomes. In the current approach, the plethora of methods on incentive based demand response is grouped according to the tools adopted to implement the incentives. The overall goal is to provide a comprehensive list of incentive design tools and be a point of inspiration for researchers in the field of incentive based demand response in smart grids.

A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems

Renewable energy sources are one key enabler to decrease greenhouse gas emissions and to cope with the anthropogenic climate change. Their intermittent behavior and limited storage capabilities present a new challenge to power system operators to maintain power quality and reliability. Additional technical complexity arises from the large number of small distributed generation units and their allocation within the power system. Market liberalization and changing regulatory framework lead to additional organizational complexity. As a result, the design and operation of the future electric energy system have to be redefined. Sophisticated information and communication architectures, automation concepts, and control approaches are necessary in order to manage the higher complexity of so-called smart grids. This paper provides an overview of the state of the art and recent developments enabling higher intelligence in future smart grids. The integration of renewable sources and storage systems into the power grids is analyzed. Energy management and demand response methods and important automation paradigms and domain standards are also reviewed.

Building power demand response methods toward smart grid

Smart grid has been drawing attention particularly when renewable generations are integrated. In order to ensure high power reliability and energy efficiency in an electrical grid, research and application has been conducted at power supply side to solve the grid critical issues: peak load and power imbalance. However, as the major end-users at power demand side, buildings can also play a significant and cost-effective role by making use of their power demand responses. Different demand response programs (e.g., time- and incentive-based) have been developed and applied for encouraging the end-users to change their energy usage behaviors expected by the grid. Generally, buildings are able to limit and/or shift the power demands according to their own considerations under the specific incentives. A comprehensive review on the building power demand response methods is still missing, although research and application has been investigated and conducted on power demand aspects concerning the building system configuration and the control strategies of power demand optimization. This article, therefore, presents a comprehensive review on the strategies, impacts, and benefits of building power demand response in a grid to systematically evaluate and make better use of their demand response potentials. The possibility of developing proper building power demand response strategies for offline and online applications of the smart grid is also discussed.