Increased Demand Response Research Articles

System planning with demand assets in balancing markets

Balancing markets will become more and more relevant with the increased volatility in the electricity system due to the increase in the renewable quota. New policies are paving the way for customers flexibility participation as demand response in reserve products. This paper contributes with an assessment of the impact of demand response participation in the reserve market when planning the electricity system’s operation and investment in new technologies. The model used has been conveniently upgraded and a set of scenarios have been raised to conduct the analysis. The residential and services sectors' consumption for heating, cooling, hot water, and electric vehicles are considered as sources of flexibility. Each one has their own modeling to represent their nature. Main findings show that demand response receives and offers more benefits for the system on the wholesale market than in balancing services, although their participation in them is quite relevant. This is due to the decrease in firm capacity investment needs thanks to reducing systems’ peak technologies and the decrease of spillages from renewables. Additionally, increasing demand response percentages in the systems lead to cost reduction. However, there is a limitation associated with an increase of CO2 emissions due to the usage of existing polluting technologies to avoid investments in storages. Finally, flexibility providers are compared to determine their flexible capabilities.

Evaluation of CO2 Emission and Local Flexibility by Water Pumping Scheduling in Water Distribution System

In recent years, the amount of the photovoltaic (PV) system has been rapidly increasing from the viewpoint of carbon neutral. There are concerns about voltage rises and grid congestion in the electrical distribution network because of large‐scale PV increases. In order to address these issues, we focused on the control of water supply pumps in the water distribution system, which is infrastructure equipment for consumers. By controlling the start/stop of the water pump, it is expected to contribute to the power distribution system by increasing Demand Response (DR) and reduce CO2 emission from the electricity used by the pump. In this paper, with the aim of constructing an optimal operation planning method for water pumps that can evaluate DR and CO2 reduction effects, the optimal operation plan is formulated as a mixed integer linear programming problem. We numerical evaluated impact of water pump operation for CO2 emission and electrical distribution network. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Data-driven surrogate optimization for deploying heterogeneous multi-energy storage to improve demand response performance at building cluster level

Energy storage such as battery and thermal energy storage is an effective approach to shift building peak load and alleviate grid stress at a building cluster level. However, due to the heterogeneous performance of different types of storage (e.g., response speed, charge/discharge efficiency and rate, storage capacity) and highly diversified energy use patterns of individual buildings, the multi-energy storage should be properly selected and optimally designed for individual buildings to achieve effective load shifting. The optimal deployment of multi-energy storage at a cluster level is a challenging optimization problem due to the nonlinear dynamic performance of the multi-energy storage and the high dimensionality as a result of a large number of buildings. To tackle the challenges, this study proposes a data-driven surrogate optimization method that optimally deploys multi-energy storage at a cluster level to minimize the building cluster energy bill under demand response programs. The method utilizes data-driven surrogate models to accurately predict demand response performance of individual buildings with multi-energy storage. An iterative optimization with automated energy-storage-option screening is developed to optimize the multi-energy storage configurations and design parameters. For a case study including 21 buildings, by optimally deploying multi-energy storage including battery, cooling TES tank, and building-integrated TES, the method reduced the building cluster energy bill by 8%–181% as compared to baseline cases. The optimal deployment method effectively identifies the buildings with better potential to adopt demand-side management and balances the pros and cons of the energy storage options, increasing demand response incentives by 12%–31%. The proposed method can be used in practice to facilitate the deployment of energy storage and improve engagement of buildings in demand response.

Trends in household demand and greenhouse gas footprints in Germany: Evidence from microdata of the last 20 years

This paper quantifies changes in the trend of greenhouse gas footprints associated with household consumption covering a period of 20 years. We combine greenhouse gas emissions related to the supply chain (embedded) and consumption (direct) with households' consumption expenditures from the sample survey of income and expenditure (EVS), and present the first comprehensive study on trends in greenhouse gas footprints of German households. We find a decay in changes in GHG footprints as a response to increases in household expenditure over time. In addition, our estimated own-price elasticities for electricity and heating indicate an increasing demand response to higher energy prices. However, this does not translate to a clear decreasing trend in the total GHG footprint because of the role of changes in the demand of other commodities and changes in expenditure levels across years. Further, comparing results of the more advanced EASI demand system with those of the more widely used QUAIDS approach, we find sizable differences.

The role of demand response in the future renewable northern European energy system

Increasing demand response (DR) from households, industry and tertiary sector may provide substantial flexibility in renewable-based energy systems, but the deployment of DR is currently limited. This study examines the future economic potential DR in the renewable rich northern European region, and also analyses power markets impacts of large-scale DR deployment in the region. For the quantifications, the energy system model BALMOREL is used, modified to include a detailed temporal modelling of available DR potentials. Results show that among the DR options analysed, space heating and water heating provide the highest shares of loads shifted. The overall demand response potential is particularly high in Norway and Sweden, due to wide-spread electric space- and water heating. Low variable costs make these DR applications economically feasible for deployment, despite high supply-side flexibility provided by regulated hydro power. DR may contribute to peak shaving of up to 18.6% of total peak load in 2050. Revenues from DR-application yield very different results depending on techno-economic parameters, potentials and the price volatility in the various analysed market areas. Results show an insignificant change in CO2 emissions between scenarios with and without demand response.

Flexible demand and supply as enablers of variable energy integration

This article analyses the impact of the interplay between the demand response, wind penetration and hydropower in the Nordic power market. Long-term equilibrium model is developed, in which the thermal power capacity adjusts to the zero-profit level over varying real-time pricing adoption rates and wind power shares. The results show that an increased demand response decreases the amount of thermal power capacity and improves the utilization rate of thermal power and the value of wind power. There is a decreasing marginal gain from an increasing demand response adoption with respect to the thermal utilization and wind value factor. However, the demand response itself does not lead to lower CO2 emissions or reduced variation in hydropower generation.

Consumer Savings, Price, and Emissions Impacts of Increasing Demand Response in the Midcontinent Electricity Market

This paper estimates consumer savings, CO2 emissions reductions, and price effects from increasing demand response (DR) dispatch in the Midcontinent Independent System Operator (MISO) electricity market. To quantify market effects, we develop a dynamic supply and demand model to explore a range of DR deployment scenarios. The study is motivated by the existence of regulatory and market rule barriers to market-based deployment of DR resources in the MISO region. We show annual consumer savings from increased market-based DR can vary from $1.3 million to $17.6 million under typical peak operating conditions, depending on the amount of DR resources available for market dispatch and the frequency of deployment. Consumer savings and other market effects increase exponentially during atypical periods with tight supply and high prices. Additionally, we find that DR deployment often reduces CO2 emissions, but the magnitude of emissions reductions varies depending on the emissions content of marginal generation at the time and location of deployment. The results of this study suggest regulators and other stakeholders should focus policy efforts to reducing regulatory barriers to DR deployment in wholesale markets, particularly in locations that experience high price spikes, to improve market efficiency and achieve cost savings for consumers.

A two-layer model for dynamic pricing of electricity and optimal charging of electric vehicles under price spikes

Pilot projects in power networks conducted across continents have established the benefits of dynamic pricing by inducing increased demand response. However, a key hurdle in the growth of demand response is the lack of widespread availability of advanced metering infrastructure, which has stymied the adoption of dynamic pricing. We believe that this hurdle will be partially addressed by the growth of electric vehicles (EVs), as smart and connected EV parking lots will be a provider of demand response. We develop a two-layer optimization model that simultaneously determines dynamic pricing policy for the system operator and demand response strategies for the EV parking lots. The model minimizes the cost to consumers, while ensuring the system operator's revenue neutral status and addressing real-time price uncertainties. A variant of the 5-bus PJM network is used to demonstrate model implementation. Numerical results show that for a low to moderate price spike scenario, dynamic pricing with demand response from EVs alone can lower the daily average consumer cost of 1.42% compared to the cost of flat pricing. A cost reduction of 6.5% is achieved when price spikes are relatively high. Computational challenges of implementing our model for real networks are discussed in the concluding remarks.

Flexible Demand and Flexible Supply As Enablers of Variable Energy Integration

This article analyses the impact of the interplay between the demand response, wind penetration and hydropower in the Nordic power market. We develop a long-term equilibrium model in which the thermal power capacity adjusts to the zero-profit level over varying real-time pricing adoption rates and wind power shares. Our results show that an increased demand response decreases the amount of thermal power capacity and improves the utilization rate of thermal power and the value of wind power. We show a decreasing marginal gain from an increasing demand response adoption with respect to the thermal utilization and wind value factor. However, the demand response itself does not lead to lower CO2 emissions or reduced variation in hydropower generation.

Efficient Algorithm for Scalable Event-Based Demand Response Management in Microgrids

Demand response management has become one of the key enabling technologies for smart grids. Motivated by the increasing demand response incentives offered by service operators, more customers are subscribing to various demand response programs. However, with growing customer participation, the problem of determining the optimal loads to be curtailed in a microgrid during contingencies within a feasible time frame becomes computationally hard. This paper proposes an efficient approximation algorithm for event-based demand response management in microgrids. In event-based management, it is important to curtail loads as fast as possible to maintain the stability of a microgrid during the islanded mode in a scalable manner. A simple greedy approach is presented that can rapidly determine a close-to-optimal load curtailment scheme to maximize the aggregate customer utility in milliseconds for a large number of customers. This paper further derives a novel theoretical guarantee of the gap between the proposed efficient algorithm and the optimal solution (that may be computationally hard to obtain). The performance of algorithm is corroborated extensively by simulations with up to thousands of customers. For the sake of practicality, the proposed event-based demand response management algorithm is applied to a feeder from the Canadian benchmark distribution system. The simulation results demonstrate that the proposed approach efficiently optimizes microgrid operation during islanded mode while maintaining appropriate voltage levels and network constrains.

An Evaluation of Intraday Trading and Demand Response for a Predominantly Hydro-Wind System Under Nordic Market Rules

Many countries are planning for a large-scale expansion of wind power. This development will have a significant impact on power system operation and economics. One of the challenges is that the difficulty to forecast wind power generation will increase the need for real-time balancing. This paper presents a study of how the impact of wind power forecast errors can be reduced by changes in the market design. The study is based on the conditions in the Nordic electricity market. A characteristic of this market is that there is a large share of flexible hydro generation; hence, ramp and unit commitment constraints rarely constrain dispatch. The need for regulation during real-time is provided in a voluntary real-time balancing market, where players can be compensated for their redispatch costs. Case studies are presented which show that a shift from day-ahead to intraday trading and increased demand response can improve the performance when the share of wind power is increasing.

A stochastic multiscale model for electricity generation capacity expansion

Long-term planning for electric power systems, or capacity expansion, has traditionally been modeled using simplified models or heuristics to approximate the short-term dynamics. However, current trends such as increasing penetration of intermittent renewable generation and increased demand response requires a coupling of both the long and short term dynamics. We present an efficient method for coupling multiple temporal scales using the framework of singular perturbation theory for the control of Markov processes in continuous time. We show that the uncertainties that exist in many energy planning problems, in particular load demand uncertainty and uncertainties in generation availability, can be captured with a multiscale model. We then use a dimensionality reduction technique, which is valid if the scale separation present in the model is large enough, to derive a computationally tractable model. We show that both wind data and electricity demand data do exhibit sufficient scale separation. A numerical example using real data and a finite difference approximation of the Hamilton–Jacobi–Bellman equation is used to illustrate the proposed method. We compare the results of our approximate model with those of the exact model. We also show that the proposed approximation outperforms a commonly used heuristic used in capacity expansion models.

Introducing a demand-based electricity distribution tariff in the residential sector: Demand response and customer perception

Increased demand response is essential to fully exploit the Swedish power system, which in turn is an absolute prerequisite for meeting political goals related to energy efficiency and climate change. Demand response programs are, nonetheless, still exceptional in the residential sector of the Swedish electricity market, one contributory factor being lack of knowledge about the extent of the potential gains. In light of these circumstances, this empirical study set out with the intention of estimating the scope of households' response to, and assessing customers' perception of, a demand-based time-of-use electricity distribution tariff. The results show that households as a whole have a fairly high opinion of the demand-based tariff and act on its intrinsic price signals by decreasing peak demand in peak periods and shifting electricity use from peak to off-peak periods.

Potential of Hourly Settlements in the Residential Sector of the Swedish Electricity Market—Estimations of Risk Reduction and Economic Result

Increased demand response is essential in order to boost the effectiveness of the Swedish power market. The all-embracing installation of automatic meter reading systems enables power suppliers to introduce hourly settlements in the residential sector. The aim of the study has been to assess the impact of electricity retailers' physical and financial risk in customer segments with different heating systems as well as to estimate the potential of the electricity contract “Fixed price with the right to return” in terms of economic consequences and risk management. To that end, computer simulations using empirical data have been employed. The results show that the physical price and volume risk, which constitute the greatest risk of suppliers, are most severe in households whose main heating system consists of a geothermal heat pump and less serious in households that are less dependent on electricity for heating. Hourly settlements in the residential electricity market have proven to be advantageous to retailers from both an economic and risk reducing point of view and provide a superior opportunity for customers, and those with an electric heating installation in particular, to lower their electricity expenses substantially.