Field demonstration of automated demand response for both winter and summer events in large buildings in the Pacific Northwest

There are growing strains on the electric grid as cooling peaks grow and equipment ages. Increased penetration of renewables on the grid is also straining electricity supply systems and the need for flexible demand is growing. This paper summarizes results of a series of field test of automated demand response systems in large buildings in the Pacific Northwest. The objective of the research was twofold. One objective was to evaluate the use demand response automation technologies. A second objective was to evaluate control strategies that could change the electric load shape in both winter and summer conditions. Winter conditions focused on cold winter mornings, a time when the electric grid is often stressed. The summer test evaluated DR strategies in the afternoon. We found that we could automate both winter and summer control strategies with the open automated demand response communication standard. The buildings were able to provide significant demand response in both winter and summer events.     

Simulating residential demand response: Improving socio-technical assumptions in activity-based models of energy demand

Demand response is receiving increasing interest as a new form of flexibility within low-carbon power systems. Energy models are an important tool to assess the potential capability of demand side contributions. This paper critically reviews the assumptions in current models and introduces a new conceptual framework to better facilitate such an assessment. We propose three dimensions along which change could occur, namely technology, activities and service expectations. Using this framework, the socio-technical assumptions underpinning ‘bottom-up’ activity-based energy demand models are identified and a number of shortcomings are discussed. First, links between appliance usage and activities are not evidence-based. We propose new data collection approaches to address this gap. Second, aside from thermal comfort, service expectations, which can be an important source of flexibility, are under-represented and their inclusion into demand models would improve their predicative power in this area. Finally, flexibility can be present over a range of time scales, from immediate responses, to longer term trends. Longitudinal time use data from participants in demand response schemes may be able to illuminate these. The recommendations of this paper seek to enhance the current state-of-the-art in activity-based models and to provide useful tools for the assessment of demand response.     

Estimating short and long-term residential demand for electricity: New evidence from Sri Lanka

This study investigates the short-run dynamics and long-run equilibrium relationship between residential electricity demand and factors influencing demand – per capita income, price of electricity, price of kerosene oil and price of liquefied petroleum gas – using annual data for Sri Lanka for the period, 1960–2007. The study uses unit root, cointegration and error-correction models. The long-run demand elasticities of income, own price and price of kerosene oil (substitute) were estimated to be 0.78, ? 0.62, and 0.14 respectively. The short-run elasticities for the same variables were estimated to be 0.32, ? 0.16 and 0.10 respectively. Liquefied petroleum (LP) gas is a substitute for electricity only in the short-run with an elasticity of 0.09. The main findings of the paper support the following (1) increasing the price of electricity is not the most effective tool to reduce electricity consumption (2) existing subsidies on electricity consumption can be removed without reducing government revenue (3) the long-run income elasticity of demand shows that any future increase in household incomes is likely to significantly increase the demand for electricity and (4) any power generation plans which consider only current per capita consumption and population growth should be revised taking into account the potential future income increases in order to avoid power shortages in the country.     

Fostering economic demand response in the Midwest ISO

The Midwest Independent System Operator (MISO) runs the power grid in 14 states and one Canadian province and has a peak demand of some 116,000 MW. Its operational area is richly supplied with reliability-triggered demand response programs such as direct load control of residential appliances and curtailable/interruptible rates for commercial and industrial customers. However, economic demand response programs are lacking. This paper discusses three different ways in which such programs can be introduced in the wholesale energy markets run by MISO. These include, first, an approach in which utilities and load serving entities move retail customers to dynamic pricing and other time-based pricing rates; second, an approach in which these same entities and possibly third-parties bid price responsive demand curves into the wholesale market; and third, an approach in which demand response is bid as a supply resource into the wholesale market.     

Evaluating the efficiency of integrated systems in the process industry–Case: Steam cracker

A literature survey indicates that decisions about whether to reject or accept an investment are largely based on financial factors. The survey also indicates that a comparison of different efficiency improvement studies is difficult because their results are not readily commensurable. From the management perspective, more information on the physical aspects affecting efficiency is needed. From the engineering perspective, studies on integrated processes deal with a number of criteria that are often in conflict with each other, and for which information is difficult to produce.This paper discusses ways to analyse efficiency improvement studies in integrated industrial processes. The paper demonstrates how these analyses could be made more comprehensive. The paper presents a case study where test runs and simulations were carried out to improve process efficiency by adjusting process operating parameters. The case study was re-evaluated using a novel approach that has been developed to assess the efficiency of integrated systems. This includes considerations from three design perspectives: energy efficiency, material efficiency and operational efficiency, which, when analysed simultaneously, result in a more complete set of design and evaluation criteria.     

The optimization of demand response programs in smart grids

The potential to schedule portion of the electricity demand in smart energy systems is clear as a significant opportunity to enhance the efficiency of the grids. Demand response is one of the new developments in the field of electricity which is meant to engage consumers in improving the energy consumption pattern. We used Teaching & Learning based Optimization (TLBO) and Shuffled Frog Leaping (SFL) algorithms to propose an optimization model for consumption scheduling in smart grid when payment costs of different periods are reduced. This study conducted on four types residential consumers obtained in the summer for some residential houses located in the centre of Tehran city in Iran: first with time of use pricing, second with real-time pricing, third one with critical peak pricing, and the last consumer had no tariff for pricing. The results demonstrate that the adoption of demand response programs can reduce total payment costs and determine a more efficient use of optimization techniques.     

Area price and demand response in a market with 25% wind power

Denmark, east and west of the Great Belt are bidding areas with separate hourly area prices for the Nord Pool power exchange, covering four Nordic countries and parts of Germany. The share of wind power has now increased to 25% on an annual basis in western Denmark. This has a significant impact not only on the electricity wholesale prices, but also on the development of the market. Hourly market data are available from the website of Danish TSO from 1999. In this paper these data are analysed for the period 2004–2010. Electricity generators and customers may respond to hourly price variations, which can improve market efficiency, and a welfare gain is obtained. An important limitation for demand response is events of several consecutive hours with extreme values. The analysis in this paper is a summary and update of some of the issues covered by the EU RESPOND project. It shows that extreme events were few, and the current infrastructure and market organisation have been able to handle the amount of wind power installed so far. This recommends that geographical bidding area for the wholesale electricity market reflects external transmission constraints caused by wind power.     

Indications for a changing electricity demand pattern: The temperature dependence of electricity demand in the Netherlands

This study assesses the electricity demand pattern in the relatively temperate climate of the Netherlands (latitude 52°30′N). Daily electricity demand and average temperature during the period from 1970 until 2007 are investigated for possible trends in the temperature dependence of electricity demand. We hypothesize that the increased use of cooling applications has shifted the temperature dependence of electricity demand upwards in summer months. Our results show significant increases in temperature dependence of electricity demand in May, June, September, October and during the summer holidays. During the period studied, temperature dependence in these months has shifted from negative to positive, meaning that a higher temperature now leads to an increased electricity demand in these months, rather than a decreased demand as observed historically. Although electricity demand in countries with moderate summer temperatures such as the Netherlands generally peaks in winter months and shows a minimum in summer months, this trend may signal the development of an additional peak in summer, especially given the expected climatic change. As power generating capacity may be negatively influenced by higher temperatures due to decreasing process cooling possibilities, an increasing electricity demand at higher temperatures may have important consequences for power generation capacity planning and maintenance scheduling.     

Stock-Watson dynamic OLS (DOLS) and error-correction modelling approaches to estimating long- and short-run elasticities in a demand function: new evidence and methodological implications from an application to the demand for coal in mainland China

In this paper robust elasticity estimates of coal demand for China are derived using annual data 1953–1992. In so doing, we illustrate the use of a powerful yet practically convenient and recently developed modelling procedure devised by Stock and Watson (known as Dynamic OLS (DOLS)), who provide evidence, based on Monte Carlo simulations, of this estimator being superior in small samples compared to a number of alternative estimators, as well as being able not only to accommodate higher orders of integration but also to account for possible simultaneity within regressors of a potential demand system. Furthermore, cointegration and error-correction methods are employed to derive short-run price and income elasticities. Estimated results are quite robust not only in terms of statistical prowess but also in terms of economic intuition and indicate that, over the long run, both price and income elasticities are close to unity. While short-run price and income elasticities are less (in absolute value) than their long-run counterparts, there seems to be some divergence in short-run parameters from a subsample analysis. Overall, results seem to imply that for China, coal consumption should remain relatively constant as future modernization strategies for economic development are pursued. In addition, the study has clear methodological implications for estimating the long- and short-run elasticities in a demand function in general, and in a wide variety of fields in future applied research.     

An economic welfare analysis of demand response in the PJM electricity market

We analyze the economic properties of the economic demand-response (DR) program in the PJM electricity market in the United States using DR market data. PJM's program provided subsidies to customers who reduced load in response to price signals. The program incorporated a “trigger point”, at a locational marginal price of $75/MWh, at or beyond which payments for load reduction included a subsidy payment. Particularly during peak hours, such a program saves money for the system, but the subsidies involved introduce distortions into the market. We simulate demand-side bidding into the PJM market, and compare the social welfare gains with the subsidies paid to price-responsive load using load and price data for year 2006. The largest economic effect is wealth transfers from generators to non price-responsive loads. Based on the incentive payment structure that was in effect through the end of 2007, we estimate that the social welfare gains exceed the distortions introduced by the subsidies. Lowering the trigger point increases the transfer from generators to consumers, but may result in the subsidy outweighing the social welfare gains due to load curtailment. We estimate that the socially optimal range for the incentive trigger point would be $66–77/MWh.     

Renewable generation and demand response integration in micro-grids: development of a new energy management and control system

The aim of this research resides in the development of an energy management and control system to control a micro-grid based on the use of renewable generation and demand resources to introduce the application of demand response concepts to the management of micro-grids in order to effectively integrate the demand side as an operation resource for the grid and improve energy efficiency of the elements. As an additional result, the evaluation of reductions in the total amount of CO₂ emitted into the atmosphere due to the improvement of the energy efficiency of the system is assessed.     

The effect of the London urban heat island on building summer cooling demand and night ventilation strategies

This paper investigates the effect that increased air temperature due to the London heat island has on the effectiveness of stack night ventilation strategies for office buildings. Stack ventilation was investigated as the most suitable night ventilation strategy because this is largely independent of wind variations affected by local urban morphology. The paper presents a summary of the results of air temperature measurements carried out in London in 1999/2000 which were used to quantify the London Urban Heat Island Intensity. It then presents data for two representative weeks, one with extreme hot weather and one with typical hot weather in the centre of the London heat island and a rural reference site. These data are used to carry out a parametric analysis by using a thermal and air flow simulation tool specifically designed for offices in SE England. A reference and optimised office module are described. A comparison of the building types based in the same location suggests that during the typical hot week the rural reference office has 84% energy demand for cooling compared to a similar urban office. A rural optimised office would not need any artificial cooling and would be able to maintain temperatures below 24 °C. An urban optimised office would not be able to achieve this. A rural optimised office would need 42% of the cooling required for an optimised urban office. A comparison of the optimised to the reference office module suggests that an urban optimised office reduces the cooling demand to 10% of the urban reference office.     

计及需求响应的微电网“源-储-荷”两级协调运行优化模型

微电网的协调运行不仅要考虑整体最优化,也应兼顾微电网内部主体的经济效益最大化。文章计及用户需求响应和相关组件约束,建立了微电网"源-储-荷"两级协调运行的双层优化模型;上层规划模型以负荷转移策略前后的相对效益最大化为目标,下层规划模型以发电侧经济效益最大化为目标;以某微电网作为算例,验证了模型的有效性和合理性。仿真结果表明,可控负荷和储能系统的参与,可增强微电网的灵活性,减小负荷峰谷差;通过两级协调规划,有效地提高了微电网的整体综合效益和发电侧收益。     

Automated demand response system in lighting for optimization of electricity cost

Daylighting is an important factor in improving visual comfort and energy efficiency. Lighting control using daylighting can reduce energy consumption in buildings. This thesis proposes an automatic demand response system for lighting based on wireless sensor networks (WSN) in order to reduce the peak electricity demand according to the stage of electricity rate with real-time pricing (RTP), time of use pricing (TOUP), and critical peak pricing (CPP). The proposed system automatically controls the slat angle of the venetian blind with a cut-off angle according to the altitude of the sun, automatically executing light dimming according to measured current luminance to remove an unpleasant glare caused by daylighting. The target illuminance of area at this time is set at a minimum illuminance required for the work execution in the office during the time zone where the electricity load is high to save the lighting energy cost, while a maximum illuminance is set during the time zone where the electricity load is low according to the real-time electricity pricing stages obtained through the advanced metering infrastructure (AMI) in order to improve the work efficiency of the occupants. In this study, two testbeds having the same environments, as well as a control system targeting a fixed illuminance per price system, were established. The illuminance energy consumption and cost were then measured and the effect of the proposed illuminance system was evaluated.     

Analysis of demand response and wind integration in Germany's electricity market

Demand response represents an additional option for reserve capacity as first market experiences have demonstrated. An analysis for Germany shows capacities up to 3 GW and costs starting at 30 euro/MWh in the industrial sector, 8 GW in the commercial sector and more than 20 GW in the residential sector including night storage heating. Simulations of the German power system showed that using these potentials together with improved wind power predictions can limit the additional balancing costs in Germany to below 2 euro/MWh feed-in by wind turbines with 48 GW wind power in 2020.     

Technical demand response potentials of the integrated steelmaking site of Tata Steel in IJmuiden

Power generation from intermittent renewable energy sources in northwest Europe is expected to increase significantly in the next 20 years. This reduces the predictability of electricity generation and increases the need for flexibility in electricity demand. Data on demand response (DR) capacities of electricity-intensive consumers is limited for most countries. In this paper, we evaluate the DR potential that can be provided to the Dutch national grid by the integrated steelmaking site of Tata Steel in IJmuiden (TSIJ). TSIJ generates electricity from its works arising gases (WAGs). The DR potentials are evaluated by using a linear optimisation model that calculates the optimal allocation of WAGs of TSIJ in case of a call for DR by the transmission system operator. The optimisation is done subject to the technical constraints of the WAG distribution network, WAG storage capacities, the on-site demand for WAGs and the ramp-up rate of the power plant that runs on WAGs. Results show that TSIJ can supply 10 MW for two programme time units (equal to 15-min period in the Netherlands) of positive DR capacity (demand reduction) with an availability rate of 97%. This is not sufficient for participating in the current emergency capacity programs in the Netherlands, which require at least 20 MW for longer than one programme time unit. Tata Steel can provide 20 MW DR capacity with an availability rate of 65%. The negative DR capacity (demand increase) of Tata Steel in IJmuiden is found to be 20 MW supplied for three programme time units and four programme time units with doubling of blast furnace gas storage capacities.     

Optimal real time pricing in an agent-based retail market using a comprehensive demand response model

In this paper, a weighted combination of different demand vs. price functions referred to as Composite Demand Function (CDF) is introduced in order to represent the demand model of consuming sectors which comprise different clusters of customers with divergent load profiles and energy use habitudes. Derived from the mathematical representations of demand, dynamic price elasticities are proposed to demonstrate the customers’ demand sensitivity with respect to the hourly price. Based on the proposed CDF and dynamic elasticities, a comprehensive demand response (CDR) model is developed in this paper for the purpose of representing customer response to time-based and incentive-based demand response (DR) programs. The above model helps a Retail Energy Provider (REP) agent in an agent-based retail environment to offer day-ahead real time prices to its customers. The most beneficial real time prices are determined through an economically optimized manner represented by REP agent’s learning capability based on the principles of Q-learning method incorporating different aspects of the problem such as price caps and customer response to real time pricing as a time-based demand response program represented by the CDR model. Numerical studies are conducted based on New England day-ahead market’s data to investigate the performance of the proposed model.     

Residential response to critical-peak pricing of electricity: California evidence

This paper analyzes data from 483 households that took part in a critical-peak pricing (CPP) experiment between July and September 2004. Using a regression-based approach to quantify hourly baseline electric loads that would have occurred absent CPP events, we show a statistically significant average participant response in each hour. Average peak response estimates are provided for each of twelve experimental strata, by climate zone and building type. Results show that larger users respond more in both absolute and percentage terms, and customers in the coolest climate zone respond most as a percentage of their baseline load. Finally, an analysis involving the two different levels of critical-peak prices – $0.50/kWh and $0.68/kWh – indicates that households did not respond more to the higher CPP rate.