Demand Response Participation Research Articles

Energy management systems for hybrid distributed generation sources in grid connected and stand-alone micro-grids

This paper proposes energy management systems for micro-grids. In recent years, the use of renewable energy sources in micro-grids has become an effective means of power decentralization especially in remote areas where the extension of the main power grid is an impediment. But a mixture of renewable energy sources and conventional generation poses serious challenges in the operation and control of micro-grids as a result of the uncertainty associated with renewable sources. Therefore, excellent energy management with regards to the power production, control, reliability, and consumption is needed in the power system. The main objective of the energy management system is to minimize the system cost as well as meeting the demand. In order to take into account the uncertainties of distributed generators (DGs) and load consumption, demand response participation and load shedding is taken into account. Two different types of algorithms are used to solve the smart energy management system of the micro-grid. To minimize system costs, which includes unit commitment and demand response, a genetic algorithm is used. To further balance the supply and demand during extreme cases, load shedding is employed through the use of the artificial neural network. The simulation results displayed corroborate the merit of the proposed method.

Resilient Distributed Real-Time Demand Response via Population Games

The proliferation of high powered electric devices is a driving force in the rising of peak power demand from electric power utilities. One way to accommodate these rising consumption patterns involves the deployment of high capacity dispatchable, but largely unsustainable peak generation systems. To avert these extravagant costs and the likelihood of grid overload, demand response (DR) strategies can be employed to curtail overall consumption, thus reducing peak patterns. In this paper, we propose a distributed real-time DR approach. The proposed method fosters seamless cooperation between DR participants for rapid convergence to expected aggregate load curtailment, while accounting for individual consumer satisfaction needs. We assess this paper through theoretical analysis based on population game theory and simulations to demonstrate its inherent flexibility, scalability, and resilience making it attractive for practical widespread deployment.

Identification of Value Proposition and Development of Innovative Business Models for Demand Response Products and Services Enabled by the DR-BOB Solution

The work presented is the result of an ongoing European H2020 project entitled DR-BOB Demand Response in Blocks of Buildings (DR-BOB) that seeks to integrate existing technologies to create a scalable solution for Demand Response (DR) in blocks of buildings. In most EU countries, DR programs are currently limited to the industrial sector and to direct asset control. The DR-BOB solution extends applicability to the building sector, providing predictive building management in blocks of buildings, enabling facilities managers to respond to implicit and explicit DR schemes, and enabling the aggregation of the DR potential of many blocks of buildings for use in demand response markets. The solution consists of three main components: the Local Energy Manager (LEM), which adds intelligence and provides the capacity for predictive building management in blocks of buildings, a Consumer Portal (CP) to enable building managers and building occupants to interact with the system and be engaged in demand response operations, and a Decentralized Energy Management System (DEMS®, Siemens plc, Nottingham, England, UK), which enables the aggregation of the DR potential of many blocks of buildings, thus allowing participation in incentive-based demand response with or without an aggregator. The paper reports the key results around Business Modelling development for demand response products and services enabled by the DR-BOB solution. The scope is threefold: (1) illustrate how the functionality of the demand response solution can provide value proposition to underpin its exploitation by four specific customer segments, namely aggregators and three types of Owners of Blocks of Buildings in different market conditions, (2) explore key aspects of the Business Model from the point of view of a demand response solution provider, in particular around most the suitable revenue stream and key partnership, and (3) assess the importance of key variables such as market maturity, user engagement, and type of blocks of buildings as drivers to market penetration and profitability. The work presented is framed by the expected evolution of DR services in different market contexts and the different relationships between the main stakeholders involved in the DR value chain in different EU countries. The analysis also relies on the results of interviews conducted at the fours pilot sites of the DR-BOB project with key representatives of the management, operations, and marketing. These are used to better understand customer needs and sharpen the value proposition.

Restoring desired voltage security margin based on demand response using load-to-source impedance ratio index and PSO

Demand Response (DR) is an active role of end-users in responding to signals coming from the market or to emergency signals coming from system operators, asking for a load reduction to help facing criticalities and reduce blackout risks. Load reduction is one of the typical DR participation methods for large and small consumers which is generally considered as a final defence strategy against system instability when voltage collapse is anticipated and may potentially result in blackout. In this paper, an algorithm concerning load reduction based DR is proposed. Indeed, a two-state algorithm based on finding optimal load reduction pattern and estimating threshold voltage of initiating DR is presented to prevent power system from voltage collapse. In this regard, load-to-source impedance ratio (ZL/XTh) is adopted as an index for evaluating voltage security margin (VSM) and is utilized in an optimization algorithm aimed at obtaining minimum load to be cut down. The proposed approach has been implemented in IEEE 39-bus network demonstrating its effectiveness and applicability.

A new method for cost-effective demand response strategy for apartment-type factory buildings

A demand response (DR) program for buildings is widely considered an effective way to improve energy efficiency using various potential resources, such as heating, ventilation, and air conditioning (HVAC) systems and distributed generators. This study presents a method for a cost-effective DR strategy for apartment-type factory buildings composed of individually owned cells. An apartment-type factory building has high energy density but there is limited control over the energy usage of each cell. To design the DR strategy, an energy response factor is defined, taking into account the energy consumption changes of each cell in relation to the outdoor temperature. Based on that, an optimization problem for the DR program participation is formulated by minimizing the total costs and an optimal DR strategy is proposed. The proposed DR strategy is based on the selection of the cells for the DR participants and their capacity. A case study using three apartment-type buildings in Korea shows that the proposed DR strategy can achieve the cost reduction of up to 67.4% on average compared to a conventional DR approach.

Coordinate Optimization of the Distribution Network Electricity Price, Energy Storage Operation Strategy, and Capacity under a Shared Mechanism

The large scale deployment of renewable generation is generally seen as the most promising option for displacing fossil fuel generators. A challenge in integrating renewable energy resources (RERs) for distribution networks is to find approaches that ensure the long term sustainability and economic profit of the Distribution Company (DisCo). In this paper, considering the air condition load demand side response, a coordinate optimization of the energy storage capacity and operation strategy is presented to maximize the economic profit of the DisCo. The operation strategy in the optimization is divided into two parts. Under the normal state, a price-based air condition quick response strategy is proposed, with both the comfort and economic efficiency of the users taken into account. Under the fault state, a sharing strategy of Generalized Demand Side Resources (GDSRs) is proposed to improve the utilization level of equipment based on the reliability insurance. Finally, the optimization is carried out on an improved IEEE-33 bus test system. The simulation results verify the effectiveness of the proposed method and discuss the effect of the load demand response participation rate on energy storage configuration. At the same time, the effect of GDSRs on the safe load rate of the line is also presented. The research provides a reference for the optimization and utilization of GDSRs.

Chance constrained unit commitment considering comprehensive modelling of demand response resources

This study proposes an incentive-based demand response (IDR) unit commitment model considering different types of demand response (DR) resources. In the proposed IDR dispatch model, (i) different load characteristic patterns of DR users can be included, such as transfer-type, shift-type and clip-type electricity users, and (ii) the uncertainty of DR participation behaviour is considered in the system reserve electricity using chance constrained programming. Simulation results for the Pennsylvania–New Jersey–Maryland (PJM) 5-bus system and the Institute of Electrical and Electronics Engineers (IEEE) 118-bus system indicate that the proposed model can achieve optimal DR scheduling while considering both economics and system reliability when high-quality DR resources are limited. Moreover, the unique scheduling features of DR must be considered in addition to economics and flexibility when dispatching DR resources; the uncertainty of DR can affect the highest confidence level of system operation. In addition, some significant coefficients of the special dispatch constraints of IDR clearly influence the performance of IDR resources.

Towards the Interactive Effects of Demand Response Participation on Electricity Spot Market Price

Abstract The electricity market is threatened by supply scarcity, which may lead to very sharp price spikes in the spot market. On the other hand, demand-side’s activities could effectively mitigate the supply scarcity and absorb most of these shocks and therefore smooth out the price volatility. In this paper, the positive effects of employing demand response programs on the spot market price are investigated. A demand-price elasticity based model is used to simulate the customer reaction function in the presence of a real time pricing. The demand achieve by DR program is used to adjust the spot market price by using a price regression model. SAS software is used to run the multiple linear regression model and MATLAB is used to simulate the demand response model. The approach is applied on one week data in summer 2014 of Connecticut in New England ISO. It could be concluded from the results of this study that applying DR program smooths out most of the price spikes in the electricity spot market and considerably reduces the customers’ electricity cost.

Optimal Facility Allocation and Determination of Demand Response Participation Rate Considering Uncertainties in Power Systems

Optimal Facility Allocation and Determination of Demand Response Participation Rate Considering Uncertainties in Power Systems

Demand response program in Singapore’s wholesale electricity market

Singapore is going to implement a demand response (DR) program to further enhance the efficiency and competitiveness of its electricity market. This paper aims to provide an in-depth investigation of this DR program which features demand side bidding and incentive payments. First, the current market clearing model (MCM) of Singapore’s existing wholesale market, which has no demand side bidding, is introduced. A mathematical model of the MCM is formulated to explain and solve the current market clearing process, where the energy and ancillary services are settled simultaneously through a form of auction pricing. Second, the mechanism of how the demand side bidding is incorporated into the current MCM is explained, with an emphasis on the demand side offer and the newly introduced constraints. A modified MCM with DR is then formulated. Third, the incentive payment mechanism intended to promote DR participation is elaborated. Numerical analysis is performed to demonstrate how the current MCM and MCM with DR work, as well as how the incentive payment is settled. Various numerical case studies are carried out to discuss the economic benefits from participating in the DR program.

Mind the Gap: Coordinating Energy Efficiency and Demand Response

Traditionally, energy demand-side management programs are each designed in isolation. Breaking with this tradition, we examine the interactions between long-term energy efficiency upgrades and daily demand response participation at an industrial firm. Using a representative model of the firm’s production and society’s energy generation, we show that energy efficiency and demand response act as substitutes. A larger incentive to participate in demand response leads to a reduced investment in energy efficiency. Upon re-examining the long-studied energy efficiency gap, we find that the gap is typically smaller when demand response is considered. Policies aiming to close or reduce the energy efficiency gap, such as investment subsidies and carbon taxes, may fail to achieve their desired outcomes when firms participate in demand response. We propose an Extended Demand Response Baseline Policy that simplifies the design and implementation of other energy efficiency policy initiatives by removing the firm’s disincentive to install energy efficiency in the presence of demand response.

A Cluster-Based Method for Calculating Baselines for Residential Loads

Demand response (DR) programs provide incentives for load reductions during event periods. Measuring these reductions requires estimating load if there was no event, known as baselines. Differences in metered load below the baseline are assumed to be load reductions. Baselines are typically calculated from historical data, sometimes with adjustments from pre-event periods. This paper presents a cohort-based baseline method that utilizes event-day metered load. The cohorts, a group of consumers with a similar consumption pattern, are developed from correlations between normal nonevent day loads. Baselines for DR participants are established using event-day metered load of nonparticipating cohort members. This method implicitly incorporates weather and other complex factors into the baseline. Our method is compared with several traditional methods using actual utility meter data. Baselines are calculated for each nonevent day and compared with the actual meter data on these days. The results show that cohort-based baselines are significantly more accurate than traditional methods.

Financial incentive approaches for reducing peak electricity demand, experience from pilot trials with a UK energy provider

Whilst tariff-based approaches to load-shifting are common in the residential sector, incentive-based approaches are rare. This is so, even though providing customers incentives to shape their power consumption patterns has substantial potential. This paper presents findings from an exploratory UK pilot study that trials financial payments and detailed energy feedback to incentivise load-shifting of residential electricity consumption.An intervention study was implemented measuring actual energy use by individual households as well as conducting surveys and interviews.From the trials it was found that the approaches resulted in reductions in peak time energy use. Evidence from the study found that the incentives-based approaches were able to overcome some of the barriers to response experienced in Time-of-Use studies, though less good on others. Interestingly, the height of the barriers varied by the electricity-using practice and the incentivising approach applied. The height of the barriers also varied by participant.The study concludes by identifying that broad participation in demand response is likely to require a suite of incentivising approaches that appeal to different people, a key policy finding of interest to international agencies, government, public and private sector entities.

Firming renewable power with demand response: an end-to-end aggregator business model

Environmental concerns have spurred greater reliance on variable renewable energy resources (VERs) in electric generation. Under current incentive schemes, the uncertainty and intermittency of these resources impose costs on the grid, which are typically socialized across the whole system, rather than born by their creators. We consider an institutional framework in which VERs face market imbalance prices, giving them an incentive to produce higher-value energy subject to less adverse uncertainty. In this setting, we consider an “aggregator” that owns the production rights to a VER’s output, and also signs contracts with a population of demand response (DR) participants for the right to curtail them in real time, according to a contractually specified probability distribution. The aggregator bids a day ahead offer into the wholesale market, and is able to offset imbalances between the cleared day-ahead bid and the realized VER production by curtailing DR participants’ consumption according to the signed contracts. We consider the optimization of the aggregator’s end-to-end problem: designing the menu of DR service contracts using contract theory, bidding into the wholesale market, and dispatching DR consistently with the contractual agreements. We do this in a setting in which wholesale market prices, VER output, and participant demand are all stochastic, and possibly correlated.

The effect of demand response on purchase intention of distributed generation: Evidence from Japan

Participation in demand response (DR) may affect a consumer's electric consumption pattern through consumption load curtailment, a shift in the consumption timing or increasing the utilization of distributed generation (DG). This paper attempts to provide empirical evidence of DR's effect on DG adoption by household consumers. By using the original Internet survey data of 5442 household respondents in Japan conducted in January 2015, we focus on the effect of the time-of-use (TOU) tariff on the purchasing intention of photovoltaic systems (PV). The empirical results show the following: 1) current TOU plan users have stronger PV purchase intentions than the other plan users, 2) respondents who are familiar with the DR program have relatively higher purchase intentions compared with their counterparts, and 3) when the respondents are requested to assume participation in the virtual TOU plan designed for the survey, which resembles plans currently available through major companies, 1.2% of the households have decided to purchase PV. In addition, we provide calculations of TOU's impacts on the official PV adoption and emissions reduction targets, and discuss policy recommendations to increase recognitions and participations in TOU programs.

Money for Nothing? Why FERC Order 745 Should have Died

Customer baseline load (CBL) measurement is designed to represent participants’ expected usage in a number of electricity demand response (DR) programs. Our empirical results, however, show that CBLs can be systematically higher than DR participants’ estimated load, especially for those experienced in DR activities, likely due to manipulation behaviors. Thus, the integrity of CBL may degrade over time. With an inflated CBL, the impact of DR programs may therefore be highly exaggerated, and consumers can be paid money when they are not actually reducing their demand. In particular, we design a manipulation-indicating variable “seemingly unattractive free-money opportunity” (SUFO) and discover systemwide manipulative behaviors that increase with time and are widely adopted by experienced DR participants. We suggest that policy makers in FERC, RTOs, and states regulatory agencies consider the threat of manipulation when modifying DR market rules following the Supreme Court’s recent upholding of FERC Order 745.

Automated Residential Demand Response Based on Advanced Metering Infrastructure Network

Demand response (DR) is a major contributor to grid stabilization during peak times. As the scope and functions of DR increase, DR participation of residential customers has attracted great attention. The main issue of DR design for the residential sector is to build an integrated network for exchange of DR signals between the utility operator and home users who want to participate in DR services at various locations. In this paper, we introduce an automated residential DR model based on advanced metering infrastructure (AMI) network. The internationally recognized standard protocol, known as the open automated demand response version 2.0 (OpenADR 2.0), is applied to realize a fully automated DR (ADR). In order to utilize existing AMI devices as ADR resources, the smart meter is modified while retaining current structure and functions, and various ADR devices are developed based on OpenADR 2.0 protocol. Simulation and demonstration tests are conducted to confirm conformance of the protocol and to verify load variations of home appliances under our ADR model. The developed software programs have obtained an international certification from the OpenADR Alliance.

Distributed energy systems integration and demand optimization for autonomous operations and electric grid transactions

Distributed power systems in the U.S. and globally are evolving to provide reliable and clean energy to consumers. In California, existing regulations require significant increases in renewable generation, as well as identification of customer-side Distributed Energy Resources (DER) controls, communication technologies, and standards for interconnection with the electric grid systems. As DER deployment expands, customer-side DER control and optimization will be critical for system flexibility and demand response (DR) participation, which improves the economic viability of DER systems. Current DER systems integration and communication challenges include leveraging the existing DER and DR technology and systems infrastructure, and enabling optimized cost, energy and carbon choices for customers to deploy interoperable grid transactions and renewable energy systems at scale.This paper presents a cost-effective solution to these challenges by exploring communication technologies and information models for DER system integration and interoperability. This system uses open standards and optimization models for resource planning based on dynamic-pricing notifications and autonomous operations within various domains of the smart grid energy system. It identifies architectures and customer engagement strategies in dynamic DR pricing transactions to generate feedback information models for load flexibility, load profiles, and participation schedules. The models are tested at a real site in California—Fort Hunter Liggett (FHL). The results for FHL show that the model fits within the existing and new DR business models and networked systems for transactive energy concepts. Integrated energy systems, communication networks, and modeling tools that coordinate supply-side networks and DER will enable electric grid system operators to use DER for grid transactions in an integrated system.

Impacts of House Sizes, Appliance Ratings and Usage Patterns on Demand Response Applications: A Case-Based Study

A home energy management (HEM) system is an important element that potentially facilitates automated demand response (DR) participation for residential customers. This paper aims at analyzing the impact of different house sizes, appliance ratings and usage patterns on residential DR implementation based on a set of predefined DR strategies. In particular, several simulation studies are conducted to demonstrate how an HEM system performs differently when implemented in households that have different characteristics. Results show that houses of different sizes, appliance ratings and usage patterns have different impacts on customers’ comfort level violation, and determine the levels of DR that can be implemented.

A Collaborative Design of Aggregated Residential Appliances and Renewable Energy for Demand Response Participation

Although the locational marginal price may change dramatically within a single day in competitive wholesale electricity markets, most end users are charged monthly electricity bills over flat rates. Without financial incentives, the customers are lacking of motivation to respond to the price signals, which may result in inefficient energy consumption. In Texas, Senate Bill 1125 encourages qualified residential and commercial customer classes to participate in demand response (DR) programs. This paper proposes an idea to aggregate a number of residential customers to participate in residential DR program by employing smart appliances and a home area network to shift the coincidental peak load to off-peak hours to reap financial benefits. The operation strategies for the most representative residential load types are discussed. To further reduce electricity purchase and cut electricity bills, a solar farm with energy storage system is proposed, and the control algorithm is designed accordingly. The operation strategies are simulated for a whole year, and the annual costs are calculated and compared in this paper. The results show that, by doing load control and utilizing renewable resources, the total operation cost can be reduced significantly.