Electricity Demand Management Research Articles

Consumer participation in power market balancing

With the increasing role of wind and solar power, the power balance authorities are facing a big challenge: How to manage the increasing need for fast balancing power brought on by increased penetration of variable and difficult-to-forecast renewable generation? Could more active participation by the residential customers in managing electricity demand be a smart way to go?

An Empirical Study of Electric Power Demand Control by Real-time Feedback of Consumption Levels: Case of Nushima Island Households

Abstract Electric power demand management will play an important role in the creation of smart-energy communities. We are conducting a field experiment on the real-time feedback of electric power consumption via smart meters and tablet PCs with the participation of 51 households on Nushima Island, one of Japan's remote islands. Our estimate of the effect of feedback in reducing power demand by panel data analysis has revealed that such feedback achieves a saving of 7.6 percent in electric power demand.

406 小規模産業用・業務用需要家を対象とした設備稼働スケジューリングツールにおける実務的課題(省エネルギー・エネルギー貯蔵)

The authors developed a scheduling tool for electric demand management of small scale industrial customers after the Great East Japan Earthquake. Through a survey of practical requirements for the tool, several issues have been revealed, for example, consideration of process line of factories and that of power demand profile of each facility. In this paper, we reported about the issues and discussed about the approach for them.

The effects of critical peak pricing for electricity demand management on home-based trip generation

This paper examines electricity critical peak pricing (CPP) as a measure for controlling electricity demand at critical peak times. This pricing scheme is designed to facilitate energy conservation not only inside but also outside the home. For this study, we surveyed consumer propensity to leave the home under CPP schemes and analyzed the impact of CPP on consumer cost. The results indicated that higher prices induce a higher rate of going out, while residential conditions such as population density and access to public transportation have a relatively small impact on leaving the home and average energy conservation. However, this is not always the case for aged households with limited mobility; residential conditions have a substantial effect on this segment of the population. Combined with a reduced ability to go out, electricity pricing has a greater negative impact on aged people. These results imply that improving accessibility through transportation development and urban compaction is an effective means of saving electricity alleviating the negative impact of CPP on the aged society of the future.

An Analysis of Electric Demand Management Programs in Korea

Electric demand management program has received a great deal of attention from the researchers to operate the power systems economically. It contributes to reliability of the electric power supplies and improves the efficiency of the facilities in the power system. Accordingly, the generation cost and maintenance cost are saved and electricity prices are stabilized. Currently, KEPCO (Korea Electric Power Corporation) and KPX (Korea Power Exchange) manage electric demand management program in Korea. KEPCO has managed electric load by Demand Adjustment Program of Designated Period (DAP of DP), Demand Adjustment Program of Advance Notice (DAP of AN), Direct Load Control and Average Interruptible Load. KPX has opened in the Demand Resource Market (DRM) when the operation reserves are predicted in the below of 5,000MW. Electricity supply and demand planning can be set up more stably with the exact evaluation of load management potential and peak reduction. In this paper, we analyze load management programs in the past, and represent the contributions for Electricity Supply and Demand Planning. Finally, the future development direction of electric demand management programs and demand response programs in Korea is represented.

Enhancement of building operations: A successful approach towards national electrical demand management

An approach for managing electrical demand through enhanced building operations in hot climates is evaluated and demonstrated in this paper. The approach focuses on implementing enhanced operations in government buildings, since they are easier to implement and administer. These enhanced operations included early reduction of cooling supply before the end of the occupancy period, improved time-of-day control after occupancy period and reduced lighting. A total of eight government buildings with different construction and system characteristics were selected for implementing these enhanced operations. These buildings have a total air-conditioning area of 4.39×105m2 and a combined peak electrical demand of 29.3MW. The enhanced operations resulted in demand savings of 8.90MW during the national peak hour. Temperatures build up inside the buildings were monitored and found to be within acceptable ranges.Guidelines for nationwide implementation in similar buildings were developed based on the results of this work. Implementation is estimated to reduce demand by 488MW by the year 2030, which amounts to capital savings of $585 million. These projected values would be important to adopt energy efficient policies for the country. Additional reductions in energy and fuel consumption are added benefits, which would result in large financial and environmental savings to the country. Moreover, the enhanced building operations would be an important tool to avoid any blackouts by properly reducing the peak electrical demand as well as operating the power plants with a higher thermal efficiency.

An illustration of the optimization of combined cooling heating and power systems using genetic algorithm

The performances of combined cooling, heating and power (CCHP) system are greatly dependent on its design, operation strategy and thermal and electric demands. This paper illustrates how the use of a genetic algorithm can provide speedy optimization, by applying it to two styles of buildings operated in different operation strategies. The primary energy consumptions of CCHP system following electric demand management (EDM) and thermal demand management (TDM) are firstly analyzed respectively. Then, sixteen hypothetical buildings are constructed to represent various energy demands. Primary energy saving (PES), annual total cost saving (ATCS), and CO2 emission reduction (CO2ER), are weighted to evaluate the integrated performances of CCHP system in comparison to separation production system. Finally, the optimized CCHP system for sixteen scenarios using GA are compared. Practical application: This paper provides an optimization design method for CCHP system. The performance analysis of CCHP systems running different operation modes for different buildings is believed by the authors to contribute to a significant guide for the fundamental design of CCHP systems. Although sensitivity to a number of other important design considerations such equipment performance, possible future changes in operating conditions, changes to the price or carbon intensity of grid-supplied energy etc are not addressed, the conclusions present a simple and effective direction and the proposed optimization algorithm and the evaluation method for CCHP system can be extended to other buildings.

Negotiated Learning for Smart Grid Agents: Entity Selection based on Dynamic Partially Observable Features

An attractive approach to managing electricity demand in the Smart Grid relies on real-time pricing (RTP) tariffs, where customers are incentivized to quickly adapt to changes in the cost of supply. However, choosing amongst competitive RTP tariffs is difficult when tariff prices change rapidly. The problem is further complicated when we assume that the price changes for a tariff are published in real-time only to those customers who are currently subscribed to that tariff, thus making the prices partially observable. We present models and learning algorithms for autonomous agents that can address the tariff selection problem on behalf of customers. We introduce 'Negotiated Learning', a general algorithm that enables a self-interested sequential decision-making agent to periodically select amongst a variable set of 'entities' (e.g., tariffs) by negotiating with other agents in the environment to gather information about dynamic partially observable entity 'features' (e.g., tariff prices) that affect the entity selection decision. We also contribute a formulation of the tariff selection problem as a 'Negotiable Entity Selection Process', a novel representation. We support our contributions with intuitive justification and simulation experiments based on real data on an open Smart Grid simulation platform.

デマンドレスポンスに資するリアルタイムモニタリングを用いた建物群の電力デマンドマネージメントの有効性に関する考察

This paper reports the effectiveness of electrical demand management for many buildings utilizing real-time energy monitoring system which contributes to the demand response. Reduction method of peak electrical demand for one building is limited. However, reduction methods of electrical demand for few buildings have more ideas than the method for one building. Electrical peak demands of 26 buildings were reduced 38% in 2011, summer.

거주자 구성유형 및 소득수준에 따른 주거용 건물 내 전력소비성향

In this paper, we draws tendency of the electricity consumption in residential buildings according to inhabitants Composition types and the level of incomes. it is necessary to reduce energy cost and keep energy security through the electricity demand forecasting and management technology. Progressive social change such as increases of single household, the aging of society, increases in the income level will replace the existing residential electricity demand pattern. However, Only with conventional methods that using only the energy consumption per-unit area are based on Energy final consumption data can not respond to those social and environmental change. To develop electricity demand estimation model that can cope flexibly to changes in the social and environmental, In this paper researches propensity of electricity consumption according to the type of residents configuration, the level of income. First, we typed form of inhabitants in residential that existed in Korea. after that we calculated hourly electricity consumption for each type through National Time-Use Survey performed at the National Statistical Office with considering overlapping behavior. Household appliances and retention standards according to income level is also considered.

A Microsimulation of Energy Demand and Greenhouse Gas Emissions from Plug-in Hybrid Electric Vehicle Use

A population of drivers was simulated using a microsimulation model. Consistent with the 2001 National Household Travel Survey (NHTS), a wide range of daily driving distance was observed. This heterogeneity implies that some drivers will realize greater fuel savings from driving a plug-in hybrid electric vehicle (PHEV) than others, therefore, consumers who choose to purchase PHEVs may tend to be those who drive farther than average. The model was used to examine the effects of this difference in driving by estimating fuel use, electricity demand and GHG emissions by two populations, one assigned PHEVs at random to some fraction of drivers, and the other assigned PHEVs to drivers who realized operating cost savings at least as great as the amortized incremental cost of the PHEV relative to a comparable conventional vehicle. These two populations showed different distributions of daily driving distance, with the population of PHEV drivers selected on the basis of operating cost savings driving 40% farther per day on average than average drivers. This difference indicates the possible range of driving patterns of future PHEV drivers, which should be taken into account when estimating fuel savings and GHG reductions from PHEVs. For example, if 20% of U.S. vehicles were PHEVs, we find a potential reduction of fuel use of 0.17 gal per day per vehicle if PHEVs substitute randomly for conventional vehicles, whereas the fuel savings is as large as 0.26 gal per day per vehicle if PHEVs are substituted according to operating cost savings. Similar differences in GHG emissions were estimated as well. The effects of electricity demand management on charging PHEVs was examined for these two populations. It was found for both that only a small fraction of PHEVs were impacted by interruptible electricity service (no charging permitted during peak hours). Most PHEV drivers were able to charge sufficiently during off-peak hours and saw little change in operating costs. This implies that interruptible electricity service may impact operating costs of only a small fraction of PHEV drivers.

Towards Optimal Electric Demand Management for Internet Data Centers

Electricity cost is becoming a major portion of Internet data center (IDC)'s operation cost and large-scale IDCs are becoming important consumers of regional electricity markets. IDC's energy efficiency is gaining more attention by data center operators and electricity market operators. Effective IDC electric demand management solutions are eagerly sought by all stakeholders. In this paper, a mixed-integer programming model based IDC electric demand management solution is proposed, which integrates both the impacts of locational marginal electricity prices and power management capability of IDC itself. Dynamic voltage/frequency scaling of individual server, cluster server ON/OFF scheduling, and dynamic workload dispatching are optimized while complying with all the IDC system-wide and individual heterogeneous servers' operation constraints according to the IDC applications' temporal variant workload. Reduced electricity cost can be achieved together with guaranteed QoS requirement and reliability consideration by using the proposed model. World Cup '98 data is utilized to evaluate the effectiveness of the proposed solution. According to the experimental evaluation, electricity cost could be cut by more than 20% in a peak workload period and by more than 80% in a light workload period. Besides, more than 6% electricity cost could be cut by considering the impact of electricity price difference. Experimental results also reveal that higher QoS requirement and reliability consideration could result in higher electricity cost.

Note on estimating effectiveness of demand management

This article suggests a method for estimating the effectiveness of electricity demand management and improving the accuracy of future peak load forecasting. The measurement accuracy is essential for deciding the optimal size of power plants. The measurement issue has been very controversial because the real effectiveness of demand management policy is not observable. This article uses the state space modelling technique to capture the effectiveness of the demand management. From the application of this technique to the Korea's case, this article finds that the effectiveness of the policy relatively becomes smaller than before and that the peak load forecast should be adjusted. This enhanced measurement technique will help the Korean government to have the theoretical base for the national energy plan in the future.

A Hybrid MPC Optimization approach for HVAC Direct Load Control

Direct load control posits the existence of a dynamic relationship between electric utilities and customers, enabling the utility to manage customer consumption as it deems appropriate. Thus, supply can be disconnected where necessary provided such action complies with customer-imposed requirements. Proper execution of a fully automated management plan will require a host of online tools including instrumentation, models, optimization algorithms and automation systems.This study develops a novel methodology that solves the online direct load control problem from an automation based formulation by means of hybrid model predictive control, using HVAC (Heating, Ventilating and Air Conditioning) systems as a particular case. The methodology models the thermal behavior of a standard zone in a building constrained by the customer's maximum temperature requirement.Our simulation results show that hybrid model predictive control is a viable alternative to solving the direct load control problem and can manage electricity demand while satisfying constraints set by consumers. It was also found that the controller operation is robust to simulated disturbances and uncertainties in its parameters.

Determinants of voluntary electricity demand management program participation

This paper examines the voluntary electricity demand management decisions of commercial and industrial customers (at the firm and facility level) of an electric utility serving a United States Midwestern metropolitan area. Using standard probit analysis, we find that electricity costs are a major determinant of a firm’s decision to voluntarily engage in demand management programs. Specifically, an additional $100,000 in electricity costs raises the probability of participation in an electricity demand management program by 0.3 percent. We also find that companies with multiple sites are 3.7 percent more likely to participate. In terms of demand management program participation, these results indicate a difference between organization and size that warrants further examination.

Performance comparison of combined cooling heating and power system in different operation modes

Operation mode of combined cooling heating and power (CCHP) system determines its energetic and environmental performances. This paper analyzes the energy flows of CCHP system and separated production (SP) system. The fuel energy consumptions of CCHP system following electrical demand management (EDM) and thermal demand management (TDM) are deduced respectively. Three indicators: primary energy saving, exergy efficiency and CO 2 emission reduction, are employed to evaluate the performances of CCHP system for a commercial building in Beijing, China. The feasibility analysis shows that the performance of CCHP system is strictly dependent upon building energy demands. The selection of CCHP operation modes is systemically based on building loads, CCHP system and local SP system. The calculation results conclude that CCHP system in winter under EDM achieves more benefits than in summer. The sensitivity discussion indicates that the coefficient of performance for cooling and the efficiency of electricity generation are the most sensitive variables to the energetic and environmental performances of CCHP system.

RESEARCH ON ENERGY AND WATER CONSUMPTION AT LARGE-SCALE HOSPITAL

The maintenance records at a certain large-scale hospital, the total floor area 75,311m2 hospital building and 12,793m2 health checkup center, are investigated. The monthly and daily data of electricity, gas and water consumption are obtained. The paper presents the features of the hospital and the outline of the buildings at first, and then annual or monthly electricity and gas consumption, including CGS operation results, and water consumption are analyzed. Then, using the daily data, the correlation between the energy consumption and the enthalpy of outside air, the difference of the data on holidays, and the annual characteristics of energy and water consumption at weekdays are presented. The paper also shows the daily maximum electric power and the restraint kW-value for the electricity demand management.

A strategic review of electricity systems models

Electricity systems models are software tools used to manage electricity demand and the electricity systems, to trade electricity and for generation expansion planning purposes. Various portfolios and scenarios are modelled in order to compare the effects of decision making in policy and on business development plans in electricity systems so as to best advise governments and industry on the least cost economic and environmental approach to electricity supply, while maintaining a secure supply of sufficient quality electricity. The modelling techniques developed to study vertically integrated state monopolies are now applied in liberalised markets where the issues and constraints are more complex. This paper reviews the changing role of electricity systems modelling in a strategic manner, focussing on the modelling response to key developments, the move away from monopoly towards liberalised market regimes and the increasing complexity brought about by policy targets for renewable energy and emissions. The paper provides an overview of electricity systems modelling techniques, discusses a number of key proprietary electricity systems models used in the USA and Europe and provides an information resource to the electricity analyst not currently readily available in the literature on the choice of model to investigate different aspects of the electricity system.

Multicriteria decision making in electricity demand management: the case of Kuwait

Electricity demand in Kuwait has substantially increased over the years and this increase is attributed to population growth, increase in the number of buildings, and the extensive use of air-conditioning system during the very hot weather in the summer. The amount of electrical energy generated reached 48 444 308 megawatt hour (MWH) in 2007. Such growth calls for extensive investment in the continuous expansion of the existing power plants and constructing new ones. To rationalise the consumption of electricity, several conservation policies have to be implemented. In this work, we have attempted to diagnose such problem and solicit expert opinions in order to provide the proper remedies. Because the problem comprises several criteria that are subjective in nature, multicriteria decision-making approaches were suggested. The Analytical Hierarchy Process (AHP) was used as a decision tool to assess the different policies that could be used to bring about electricity conservation.

Multi-criteria analysis of combined cooling, heating and power systems in different climate zones in China

The design and operation of combined cooling, heating and power (CCHP) systems are greatly dependent upon the seasonal atmospheric conditions, which determine thermal and power demands of buildings. This paper presents a mathematical analysis of CCHP system in comparison to separate system. The corresponding primary energy consumption in thermal demand management (TDM) and electrical demand management (EDM) operation modes are deduced. Three relative criteria, primary energy saving (PES), CO 2 emission reduction (CO 2ER), and annual total cost saving (ATCS) are employed to evaluate the respective performances of CCHP systems for a hypothetical building in five different climate zones from the technical, environmental and economic aspects. The results indicate that CCHP system in TDM mode in the cold area, where the building requires more heating during the year, achieves more benefit over separate system while CCHP system in EDM mode suits the building having stable thermal demand in mild climate zone.