Demand Response Providers Research Articles

Risk-Constrained Offering Strategy for Aggregated Hybrid Power Plant Including Wind Power Producer and Demand Response Provider

The unpredictable and volatile nature of wind power is the main obstacle of this generation source in short-term trading. Owing to the ability of demand side to cover wind power imbalances, aggregated loads have been presented in the literature as a good complementary resource for the wind generation. To this end, this paper proposes a technique to obtain the best offering strategy for a hybrid power plant consisting of a wind power producer and a demand response provider in the power market. In addition, conditional value-at-risk is used to limit the risk on profit variability. Finally, a detailed analysis of a realistic case study based on a wind farm in Spain has illustrated that joint operation of wind power producers and demand response providers can increase the expected profit and reduce the potential risks.

Demonstrating demand response from water distribution system through pump scheduling

Significant changes in the power generation mix are posing new challenges for the balancing systems of the grid. Many of these challenges are in the secondary electricity grid regulation services and could be met through demand response (DR) services. We explore the opportunities for a water distribution system (WDS) to provide balancing services with demand response through pump scheduling and evaluate the associated benefits. Using a benchmark network and demand response mechanisms available in the UK, these benefits are assessed in terms of reduced green house gas (GHG) emissions from the grid due to the displacement of more polluting power sources and additional revenues for water utilities. The optimal pump scheduling problem is formulated as a mixed-integer optimisation problem and solved using a branch and bound algorithm. This new formulation finds the optimal level of power capacity to commit to the provision of demand response for a range of reserve energy provision and frequency response schemes offered in the UK. For the first time we show that DR from WDS can offer financial benefits to WDS operators while providing response energy to the grid with less greenhouse gas emissions than competing reserve energy technologies. Using a Monte Carlo simulation based on data from 2014, we demonstrate that the cost of providing the storage energy is less than the financial compensation available for the equivalent energy supply. The GHG emissions from the demand response provision from a WDS are also shown to be smaller than those of contemporary competing technologies such as open cycle gas turbines. The demand response services considered vary in their response time and duration as well as commitment requirements. The financial viability of a demand response service committed continuously is shown to be strongly dependent on the utilisation of the pumps and the electricity tariffs used by water utilities. Through the analysis of range of water demand scenarios and financial incentives using real market data, we demonstrate how a WDS can participate in a demand response scheme and generate financial gains and environmental benefits.

Optimal residential smart appliances scheduling considering distribution network constraints

AbstractAs smart appliances (SAs) are more widely adopted within distribution networks, residential consumers can contribute to electricity market operations with demand response resources and reduce their electricity bill. However, if the schedules of demand response resources are determined only by the economic electricity rate signal, the schedule can be unfeasible due to the distribution network constraints. Furthermore, it is impossible for consumers to understand the complex physical characteristics and reflect them in their everyday behaviors. This paper introduces the concept of load coordinating retailer (LCR) that deals with demand responsive appliances to reduce electrical consumption for the given distribution network constraints. The LCR can play the role of both conventional retailer and aggregated demand response provider for residential customers. It determines the optimal schedules for the aggregated neighboring SAs according to their types within each distribution feeder. The optimizatio...

Demand response potential of electrical space heating in Swedish single-family dwellings

This paper investigates the potential and economics of electrical space heating in Swedish single-family dwellings (SFDs) to provide Demand Response (DR) for the electricity load in Sweden.A dynamic and detailed building-stock model, is used to calculate the net energy demand by end-use of a set of sample buildings taken as representative of all Swedish SFDs with electrical heating. A new sub-model optimizes the dispatch of heating systems on an hourly basis, for each representative building, minimizing the cost of electricity purchased from the hourly spot market.The analysis of the Swedish SFD buildings indicates a technical DR capacity potential of 7.3 GW, which is considerable and can be used for the management of intermittent electricity generation. This potential could also prove to be valuable in the operating reserve market. However, this requires that the DR, rather than being governed by a single hourly electricity price signal, would instead be subject to a more centralized control. The modeling shows that DR can be expected to result in up to 5.5 GW of decreased load and 4.4 GW of increased load, if applying current Swedish electricity prices. The modeling shows that DR shifts up to 1.46 TWh of electric heating, corresponding to 1% of total Swedish electricity demand. The potential savings from DR for individual SFDs is found to be low, 0.9–330 €/year, given current Swedish electricity prices.

Which electricity market design to encourage the development of demand response?

Demand response is a cornerstone problem in electricity markets under climate change constraints. Most liberalized electricity markets have a poor track record at encouraging the deployment of smart meters and the development of demand response. In Europe, different models are considered for demand response, from a development under a regulated regime to a development under competitive perspectives. In this paper focusing on demand response and smart metering for mid-size and small consumers, we investigate which types of market signals should be sent to demand managers to see demand response emerge as a competitive activity. Using data from the French power system over nine years, we compare the possible market design options which would enable the development of demand response. Our simulations demonstrate that under the current market rules demand response is not a profitable activity in the French electricity industry. Introducing a capacity market could bring additional revenues to demand response providers and improve incentives to put in place demand response programs in a market environment.

Long-Term Market Equilibrium in Smart Grid Paradigm With Introducing Demand Response Provider in Competition

With introducing demand response provider (DRP) in smart grid paradigm, small consumers can actively participate in various demand response programs, especially price-based programs. This newly important matter can significantly affect the power system problems, such as expansion planning. Accordingly, consideration of small consumers via DRP in long-term market equilibrium can be notably useful. This paper proposes a bi-level game framework with iterating between master and slave level to find the long-term market equilibrium in the pool-based market, including opponent generation companies (GenCos), DRP, and independent system operator (ISO). In the master level, a set of investment decisions are generated. Then, the system information is updated and the market is cleared in the slave level. After evaluating all decision-making options, the ISO selects the best option based on a tradeoff analysis. The proposed method is examined on a system with one DRP and two GenCos under ISOs supervision.

Certification prerequisites for activities related to the trading of demand response resources

Certification according to international standards brings many benefits to the society, including technical, economic and environmental aspects. In this context, this paper highlights the benefits of certification of Demand Response, including the additional credibility which provides to the trading of flexibility and higher confidence between different players. The consequence is a dynamic environment which facilitates the market acceptance of Demand Response services and products, providing significant benefits to providers and users of such services. A methodology for the systematic certification of different activities related to the transaction of Demand Response resources has been developed and it is presented here. In particular, three types of certificate have been specified, considering the certification of the entity providing the resource (Demand Response Provider), the contractual framework between the provider and the requester (Demand Response Product) and the physical platform to enable and guarantee such transaction (Demand Response Energy Service Trader).The results of this paper may help regulators and standardization bodies in the design and specification of a future norm to allow the certification of the above-mentioned activities, or a further development of existing regulation for certification of energy efficiency systems (like ISO (International Standard Organization) 50001), where certification of Demand Response activities could be complementary.

Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management

In this paper, a multi-objective energy management system is proposed in order to optimize micro-grid (MG) performance in a short-term in the presence of Renewable Energy Sources (RESs) for wind and solar energy generation with a randomized natural behavior. Considering the existence of different types of customers including residential, commercial, and industrial consumers can participate in demand response programs. As with declare their interruptible/curtailable demand rate or select from among different proposed prices so as to assist the central micro-grid control in terms of optimizing micro-grid operation and covering energy generation uncertainty from the renewable sources. In this paper, to implement Demand Response (DR) schedules, incentive-based payment in the form of offered packages of price and DR quantity collected by Demand Response Providers (DRPs) is used. In the typical micro-grid, different technologies including Wind Turbine (WT), PhotoVoltaic (PV) cell, Micro-Turbine (MT), Full Cell (FC), battery hybrid power source and responsive loads are used. The simulation results are considered in six different cases in order to optimize operation cost and emission with/without DR. Considering the complexity and non-linearity of the proposed problem, Multi-Objective Particle Swarm Optimization (MOPSO) is utilized. Also, fuzzy-based mechanism and non-linear sorting system are applied to determine the best compromise considering the set of solutions from Pareto-front space. The numerical results represented the effect of the proposed Demand Side Management (DSM) scheduling model on reducing the effect of uncertainty obtained from generation power and predicted by WT and PV in a MG.

A Dynamic Water-Filling Method for Real-Time HVAC Load Control Based on Model Predictive Control

Heating ventilation and air-conditioning (HVAC) system can be viewed as elastic load to provide demand response. Existing work usually used HVAC to do the load following or load shaping based on given control signals or objectives. However, optimal external control signals may not always be available. Without such control signals, how to make a tradeoff between the fluctuation of non-renewable power generation and the limited demand response potential of the elastic load, while still guaranteeing user comfort level, is still an open problem. To solve this problem, we first model the temperature evolution process of a room and propose an approach to estimate the key parameters of the model. Second, based on the model predictive control, a centralized and a distributed algorithm are proposed to minimize the fluctuation and maximize user comfort level. In addition, we propose a dynamic water level adjustment algorithm to make the demand response always available in two directions. Extensive simulations based on practical data sets show that the proposed algorithms can effectively reduce the load fluctuation.

Robust n–k contingency constrained unit commitment with ancillary service demand response program

Today, many Independent System Operators (ISOs) establish programs to manipulate the reserve provided by Demand Response (DR) in ancillary service markets. In this study, Ancillary Service DR (ASDR) programs, recently introduced by Federal Energy Regulatory Commission, is integrated to an n–k Contingency Constrained Unit Commitment problem to investigate the capability of the DR Providers (DRPs), newly added ancillary market participants, in mitigating the impacts of simultaneous multiple contingencies in a power system. In the proposed model, an n–k security criterion by which power balance constraint is satisfied under any contingency state comprising simultaneous outages in generation units is investigated in the presence of the ASDR programs. Demand side reserve is supplied by DRPs, which have the responsibility of aggregating and managing customer responses to offer a bid-quantity to the ISO. The proposed formulation is a Mixed Integer Programming problem based on primal-dual optimisation, reported recently in literature. In addition, a detailed discussion about versatile effects of ASDR programs on n–k security criterion is presented to demonstrate the applicability of the proposed model.

Physics-based simulation of the impact of demand response on lead-acid emergency power availability in a datacenter

This paper uses a one-dimensional, physics-based model of a valve-regulated lead-acid (VRLA) battery to examine the impact of demand response on uninterruptible power supply (UPS) availability in a datacenter. Datacenters are facilities that provide services such as cloud computing, web search, etc. They are also large electricity consumers. An energy-efficient 15 MW datacenter, for instance, may pay $1 m per month for electricity. Datacenters often utilize VRLA batteries to ensure high reliability in serving their computational demand. This motivates the paper's central question: to what extent does the use of datacenter UPS batteries for demand response affect their availability for their primary purpose (namely, emergency power)? We address this question using a physics-based model of the coupled diffusion-reaction dynamics of VRLA batteries. We discretize this model using finite differences, and simulate it for different datacenter battery pack sizes. The results show that for a typical datacenter power demand profile, a VRLA battery pack sized for UPS functionality can provide demand response with only a minimal loss of UPS availability.

Stochastic operational scheduling of smart distribution system considering wind generation and demand response programs

In this paper a stochastic operational scheduling method is proposed to schedule energy and reserve in a smart distribution system with high penetration of wind generation. The wind power and demand forecast errors are considered in this approach and the reserve is furnished by both main grid generators and responsive loads. The consumers participate in both energy and reserve scheduling. A Demand Response Provider (DRP) aggregates loads reduction offers in order to facilitate small and medium loads participation in demand response program. The scheduling approach is tested on an 83-bus distribution test system over a 24-h period. Simulation results show that the proposed stochastic energy and reserve scheduling with demand response exhibits a lower operation cost if compared to the deterministic scheduling.

A multi-agent system providing demand response services from residential consumers

High share of distributed energy resources (DER) into power systems can significantly modify the net demand profile. Error forecasts of intermittent generation of renewable energy sources (RES) along with the current inelastic behavior of the consumption can provoke considerable network operational issues, such as frequency fluctuations and voltage imbalances. Increasing the noncontrollable DER penetration in network operation requires increased flexible and dispatchable generation capacity for balancing RES generation intermittency. Demand response mechanisms can be an efficient and less costly alternative for handling the grid issues posed by high RES penetration. The aim of this paper is to demonstrate enabling Information and Communication Technologies (ICT) and operational tools for distributed demand management mechanism that allows consumers to participate in grid support without affecting their level of satisfaction. An actual household environment called Mas Roig and located in Llagostera, Spain is used to demonstrate and assess the ICT based demand response mechanism. The results of the implementation are presented and evaluated providing useful insights for a mass deployment of such mechanisms.

SCUC problem for solar/thermal power system addressing smart grid issues using FF algorithm

This paper critically reviews the reliability impacts of major smart grid resources such as solar energy and demand response (DR). It is therefore important to develop a technique for the integration of solar and thermal generation system. High solar penetration can lead to high-risk level in the power system reliability. In order to maintain the system reliability, solar power dispatch is usually controlled and the energy storage is considered for smoothing out the fluctuations. This smart grid environment gives customer the opportunity to declare their own energy requirements together with their desired reliability levels (DRL) to independent system operator (ISO) through demand response provider (DRP). Based on the proposed model, firstly, a security-constrained unit commitment (SCUC) problem for the solar/thermal power system incorporating DR is solved using binary real coded firefly (BRCFF) algorithm. Secondly, the different reserve settlement schemes in the deregulated frame work are discussed based on their demand and DRL of the DRP. The effectiveness of the proposed model is demonstrated on IEEE RTS 24 bus test system by comparing its performance with other methods reported in the literature.

Demand response in liberalized electricity markets: Analysis of aggregated load participation in the German balancing mechanism

Power systems are in the midst of radical changes as a result of steadily rising demand, advanced technology integration and increasing penetration levels of renewable energy sources. Such developments have brought about increasing recognition of consumer behavior and the provision of DR (demand response). The aggregation of small loads as a DR flexibility resource allows end-users to participate in electricity markets and aid in maintaining dynamic system stability. An analysis of the German balancing mechanism illustrates that DR is undermined by three mechanism design aspects: minimum bidding volume, minimum bid duration and binding up and down bids.

Economic evaluation of demand response in power systems with high wind power penetration

The penetration of wind power generation is expected to increase in power systems, dramatically. The unpredictable nature of the wind generation poses an obstacle to high penetration of wind energy in the electric power systems. Demand response (DR) may be considered as an efficient approach to cope with the energy unbalances caused by the wind power intermittency. Fair mechanism for pricing of the DR may increase the demand-side participation which consequently facilitates wind power integration in the power systems. This paper focuses on the economic evaluation of the DR according to its potential for mitigating the wind power forecast error in the power system operation. Demand increase, similar to the demand curtailment, is considered as a DR resource and evaluated in this paper. For this purpose, first an insight is provided into the power system operation under the high wind power penetration with the aim of extracting the DR benefits. Based on the DR benefits, a mathematical model is developed to find the maximum monetary incentive for the DR that the system operator is willing to pay to the DR providers. In the proposed model, DR's potential in reducing the cost of supplying load as well as its capability in reducing the cost of system reserve, start up and shut down of units, load shedding, and wind power spillage are considered. The results of the proposed evaluation method provide valuable information for both the system operator and demand response providers. The proposed method is implemented on an example and a realistic case study and discussions on results are presented.

Stochastic multi-objective operational planning of smart distribution systems considering demand response programs

The development of smart grids offers new opportunities to improve the efficiency of operation of Distributed Energy Resources (DERs) by implementing an intelligent Distribution Management System (DMS). The DMS consists of application systems that are used to support the DERs management undertaken by a Distribution System Operator (DSO). In this paper, a conceptual model for a Demand Response Management System (DRMS), conceived as an application system of a DMS, is presented. Moreover, an optimization tool, able to consider the available DERs (conventional or renewable Distributed Generations (DGs) and demand response) is proposed. The optimization tool uses a stochastic multi-objective method in order to schedule DERs and aims at minimizing the total operational costs and emissions while considering the intermittent nature of wind and solar power as well as demand forecast errors. In order to facilitate small and medium loads participation in demand response programs, a Demand Response Provider (DRP) aggregates offers for load reduction. The proposed scheduling approach for DERs is tested on a 69-bus distribution test system over a 24-h period.

Electric Demand Response Management for Distributed Large-Scale Internet Data Centers

This paper evaluates the electric demand response (DR) management for distributed large-scale Internet data centers (IDCs) via the stochastic optimization approach. The electric DR of IDCs refers to the capability of optimally shifting cloud service tasks among distributed IDCs. Thus, the energy consumption reduction at certain IDC locations could be considered as the DR provision capacity in day-ahead DR programs. Cloud service tasks of IDCs include processing, storage, and computing tasks, which are further categorized into interruptible and non-interruptible tasks. The proposed model determines the optimal hourly DR capabilities of individual IDCs while considering uncertain coming cloud service tasks to individual IDCs. The major contribution of this paper is to rigorously formulate the DR capability of IDCs as changes in the electricity consumption when shifting cloud service tasks among distributed IDCs in different time zones, while considering the energy consumption for providing IT service, cooling, shifting cloud service tasks, environmental impacts, and uncertain coming tasks. The proposed model would enhance the financial situation and improve the environmental impacts of distributed IDCs by participating in day-ahead DR programs. The stochastic optimization adopts scenario-based approach via the Monte Carlo (MC) simulation for minimizing the total electricity cost, which is the expected electricity payment minus the revenue from the DR provision. The proposed model is formulated as a mixed-integer linear programming (MILP) problem and solved by state-of-the-art MILP solvers. Numerical results show the effectiveness of the proposed approach for solving the optimal electric DR management problem for distributed large-scale IDCs.

The Impact of Uncertain Physical Parameters on HVAC Demand Response

Heating, ventilation and air conditioning (HVAC) units are one of the major resources providing demand response (DR) in residential buildings. A DR program requires a large population of units to make a significant impact on power grid services like peak shaving and balancing. This paper investigates the importance of various HVAC physical parameters and their distributions that affect the aggregate response of a population of units to DR signals. This is a key step to the construction of HVAC models with DR functionality, given insufficient data, to predict the DR capacity available for dispatch. The HVAC model parameters include the size of floors, insulation efficiency, the amount of solid mass in the house, and efficiency. These parameters are usually assumed to follow Gaussian or Uniform distributions over the population. The impact of uncertainty in parameter distributions are quantified through the following steps: 1) Simulate the response of an HVAC population during the transient phase and during steady state for a given DR signal; 2) Use a quasi-Monte Carlo sampling method with linear regression and Prony analysis to evaluate the sensitivity of the DR output to the uncertainty in the parameter distributions; and 3) Identify important parameters based on their impact to the aggregate HVAC response. Utilities or DR providers can use this analysis as guidance in the collection of data to derive an effective DR model.

Randomized PHEV Charging Under Distribution Grid Constraints

Plug-in Hybrid Electrical Vehicles (PHEV) are promising to improve energy efficiency and environment friendliness. However, without proper control, their charging will cause harmful impact on the power distribution grid, including load congestion and voltage drop. Instead of using centralized optimization which may need accurate predictions on key parameters, in this paper, a new decentralized random access framework is introduced to schedule the PHEV charging. The proposed distributed solution does not need accurate predictions and can be executed online. Simulation on a semi-urban residential medium voltage grid shows that our algorithm can effectively provide demand response to protect the distribution grid from bus congestion and voltage drop, and also improve its efficiency. Most importantly, this algorithm is simple to deploy.