Electricity Demand Management Research Articles

The Non-Linear Relationship between Electricity Consumption and Temperature in Taiwan: An Application for STR (Smooth Transition Regression) Model

This study builds non-linear econometric models to analyze the effects of temperature on electricity consumption in Taiwan by using the smooth transition regression (STR) model and the monthly time-series data from 1983 to 2012. The empirical results indicate that there is a non-linear relationship between electricity consumption and temperature in Taiwan. Furthermore, all the six estimated threshold temperatures are between 25.364°C and 27.156°C, and the average of threshold temperatures is 26.384°C. It implies that Taiwan’s electricity consumption has a non-linear growth if average temperature is higher than the threshold temperature. In addition, the estimated threshold temperature has policy implications for Taiwan’s policy makers, meaning that the threshold temperature in this study can serve as a reference for framing policies of managing electricity demand in Taiwan.

Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV

Cooling in the industry sector contributes significantly to the peak demand placed on an electrical utility grid. New electricity tariff structures include high charges for electricity consumption in peak hours which leads to elevated annual electricity costs for high-demanding consumers. Demand side management (DSM) is a promising solution to increase the energy efficiency among customers by reducing their electricity peak demand and consumption. In recent years, researchers have shown an increased interest in utilizing DSM techniques with thermal energy storage (TES) and solar PV technologies for peak demand reduction in industrial and commercial sectors. The main objective of the present study is to address the potential for applying optimization-based time-of-use DSM in the industry sector by using cold thermal energy storage and off-grid solar PV to decrease and shift peak electricity demands and to reduce the annual electricity consumption costs. The results show that when cold thermal energy storage and solar PV are coupled together higher annual electricity cost savings can be achieved compared to using these two technologies independently. Additionally, considerable reductions can be seen in electricity power demands in different tariff periods by coupling thermal energy storage with off-grid solar PV.

Tradeoffs between revenue and emissions in energy storage operation

Grid-level energy storage is an emerging technology that provides operational flexibility for managing electricity demand, integrating renewable energy, and improving system reliability. However, it has been established that revenue-maximizing grid-level energy storage tends to increase system emissions in current US electricity grids. In this work, we consider storage operational strategies that value both revenue and CO2 emissions to understand the tradeoffs between these two criteria. We use actual electricity prices and marginal emissions factors in a linear programming model that optimizes operation between annual revenue and CO2 emissions to find the Pareto Frontier for 22 eGRID sub-regions. We find that, in many US regions, marginal storage-induced CO2 emissions can be decreased significantly (25–50%) with little effect on revenue (1–5%). Electricity grids with larger flexibility in daily electricity prices and in marginal emissions factors have more potential to reduce annual storage CO2 emissions at low cost to storage operators. These results show that negative environmental effects of storage operation can be reduced or eliminated at low cost through voluntary or regulatory shifts in operational patterns.

An MILP formulation for the optimal management of microgrids with task interruptions

This work is focused on the optimal management of electricity and heat generation and demand in microgrids. The objective of the proposed mathematical model is to adjust energy and heat availability profiles resulting from the use of renewable energy sources and flexible energy and heat demands. The optimisation of the resulting short-term problem is addressed through a Mixed-Integer Linear Programming (MILP) mathematical model to minimise the operational cost of the microgrid. Delays in the energy demands are allowed to tackle flexible demand profiles, under penalties in the objective function. An additional characteristic was the consideration of non-constant profiles in the considered tasks. Also, this model takes into account eventual interruptions in the tasks, applying penalties in the economic objective function. The main decisions to be made includes the schedule of tasks, as well as energy and heat generation levels, purchases from and exportation to the power grid, and storage levels.

P-6-2 Webベースの需要家設備の最適運用計画ツールの開発

P-6-2 Webベースの需要家設備の最適運用計画ツールの開発

Modeling and optimal resources allocation of a novel tri-distributed generation system based on sustainable energy resources

Tri-generation systems with the aim of recycling heat dissipation of equipment and importing the heat into the energy production cycle have been considered by researchers recently because of increasing energy efficiency and decreasing environmental pollution. Many studies have been done in the field of tri-generation systems, but the studies have been more focused on centralized energy sources, such as, steam and gas turbines. Thus, required researches to move the sources of tri-generation systems toward renewable energy resources are not perfect enough. Moreover, the type of operation strategy, which is one of the important issues in investigating tri-generation system, is usually depended on assistant resources, such as, local power grid. In this paper, a novel tri-generation system based on wind and solar renewable energy resources and natural gas as the system prime movers is evaluated. Furthermore, a different operation strategy is considered to minimize the need to auxiliary sources and so the ability to use the system in remote regions, independently. Hence, wind turbines, photovoltaic (PV) modules, and solid oxide fuel cells (SOFCs) are considered as prime movers of the system. Moreover, a battery bank and heat storage tanks (HSTs) are deployed to balance the fluctuations in produced energy by wind and solar renewable resources. Hence, thermal demand management (TDM) and electrical demand management (EDM) operation strategies are considered simultaneously and defined as two possible functions to achieve a system with the ability to operate independently. In order to obtain the optimized solutions for each system component, a genetic optimization algorithm is applied to solve a resources allocation problem. The possibility of implementation in distant regions, considerable reduction in fuel consumption and pollution are the advantages of the proposed system. The numerical results demonstrate that the fossil fuels consumption and the pollution, respectively, are reduced up to 154 and 207 times more than common separated production (SP) systems. Moreover, power grid and other auxiliary systems requirements are reduced to less than 1%.

Complementary configuration and performance comparison of CCHP-ORC system with a ground source heat pump under three energy management modes

The variation of the end-user load and the constant output of power generation units (PGUs) results in a mismatch between energy supply and demand. To reduce the difference and to avoid an excess output of electricity or heat, a complementary combined cooling, heating and power- organic Rankine cycle system (CCHP-ORC) system with a ground source heat pump is configured. In this study, the CCHP-ORC system, which is operated in thermal demand management (TDM) or electricity demand management mode, is employed to analyze the operational cost (OC), annual total cost (ATC), carbon dioxide emission (CDE), primary energy consumption (PEC) and system efficiency based on a case study of a regional energy system in Sino-Singapore eco-city. Meanwhile, the performance analysis of an improved operational mode is also conducted in this study. Following this mode, the waste heat and electricity imported from grid can be minimized by controlling the operation condition of PGU and ORC with a thermal management controller (TMC-ORC mode). Monthly and annual results in the case study show that the proposed system performed well on OC, CDE and PEC reductions and the ATC are both increased except for the ATC of TMC-ORC mode. However, the total efficiency of TDM is the highest among the three operational modes. Finally, for TMC-ORC mode, sensitivity analysis is performed and results are presented with varying electricity and gas price.

Balanced charging strategies for electric vehicles on power systems

This paper presents an electric vehicle charging demand management method by modeling the demand dispatch calculation. Despite the demand shift owing to the price signal, the signal is occasionally inaccurate because of the load variability. The electricity rate has features that can shift the electric vehicle charging priorities; the application of the load fluctuation criterion is sufficient for the management plan. We expect that there is a point, wherein, both the electric vehicle users (saving costs) and the system operators (relieving loads) are satisfied with the mutually beneficial arrangements. Our method determines the balanced state in which the loads and costs are considered simultaneously with the proposing criteria. The method allows the discordance between the electrical prices and the system load fluctuations to be managed, while the time-of-use pricing and load deviation indices are accounted for. We focus on the gap corresponding to the load variation and the charging price in a daily scheme. In contrast to the typical valley filling strategies, the aim of this study is to determine and solve the mismatches in the different goals of the costs and loads, if the state is not mutually beneficial. Therefore, to ensure a system operator perspective selectively, we introduce the load weight and ranking method concepts for dispersing the charging loads, lowering the system marginal prices, and investment avoidance because electricity rates cannot describe the load curves accurately. The charging demand calculation is investigated based on the determination of the charging patterns and daily demands using the priority comparison method. The balancing strategy first fills the mutual benefit points with respect to the changing priorities and then, competes to find the balanced points. The significance of the method is that it is based on the unique relationship between two comprehensive competitive strategies. Thus, we determine that valley filling, flat load management, and regulated deviation are insufficient to describe the user and operator behaviors simultaneously.

Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives

The active management of electricity demand, also referred to as demand side management (DSM), has been recognized as an effective approach to improving power grid performance and consumer benefits. Being large electricity consumers, the power-intensive process industries play a key role in DSM. In particular, enterprise-wide optimization (EWO) for industrial DSM has emerged as a major area of interest for both researchers and practitioners. In this work, we introduce the reader to the fundamentals of power system economics, provide a definition of DSM that reflects more strongly the consumer's perspective, and present a comprehensive review of existing works on EWO for industrial DSM. The review is organized into four parts, which correspond to the four main challenges that we identify as: (1) accurate modeling of operational flexibility, (2) integration of production and energy management, (3) decision-making across multiple time and space scales, and (4) optimization under uncertainty. Finally, we highlight research gaps and future opportunities in this area.

The Power of Demand

Electrical demand management is a strategy used by utility companies to manage peak consumption. For commercial customers, utility companies often include electric demand charges for the maximum power used during a specified time period, measured in $/kW. This is in addition to the charge for power consumed measured in $/kWh. Rate structures generally vary by region and utility company, but the premise is the same. Strategies designed to avoid or minimize the impact of electric demand to reduce operating cost are not a new idea; however, these designs are used primarily in facilities that are large power users where the operation of energy consuming systems can be scheduled to take advantage of the rate structure.The energy performance of a building is dependent on choices made by the occupants about the proper use and maintenance of building systems. Thus, controlling and adjusting occupant behavior is important to ensuring the full potential of high performance facilities. Traditionally, the design team's role on a project ends when the building is built and all of the post-construction documentation is provided to the owner. Systems in modern-day buildings are complex, especially for users who lack training in architecture or engineering. These misunderstood systems are often incorrectly altered which ultimately impacts building energy consumption. Design, commissioning, and energy professionals can positively impact building performance by training the building occupants on how their systems work.The techniques for demand avoidance described in this article are so subtle that occupants rarely detect the changes. In this article, actual data and case studies are presented from educational facilities showing the cost of implementation and the return on investment (ROI). The ROI for these system improvements is commonly less than two years.

Facility Scale Energy Storage: Applications, Technologies, and Barriers

Many large facilities in the U.S. are considering the deployment of energy storage technologies for electric demand response programs. Technologies developed for facility- and campus-scale energy storage show promise for managing short-term electrical demand peaks as well as longer-period demand response events.This article's author has investigated facility and campus-scale energy storage for efficiency program administrators in the U.S. and recently completed a storage technology research report for an international consortium of utilities. This work has identified promising avenues for distributed storage. Facility-scale storage has three primary uses: 1) power quality—the monitoring and regulation of voltage fluctuations, frequency disruptions, and harmonic distortions; 2) bridging power—short-term power supply for critical demands, often used to cover time periods in which emergency generators are powering up; and 3) energy management—energy storage on a scale to support a facility or campus of buildings for extended periods of time. These systems can be responsive to utility demand programs and time-of-use rates to reduce electrical peak demand costs.All three of these facility-scale applications incorporate the development of strategies to use distributed storage for electric power continuity and demand management strategies.This article considers the technical properties of current storage systems, including flywheel, compressed air, and various battery technologies. The technical and market barriers associated with distributed storage, along with proposed paths for resolving these barriers, will also be discussed.

개방형 자동 수요 반응 시스템 보안 취약성 분석에 관한 연구

Abstract Technology to optimize and utilize the use and supply of the electric power between consumer and supplier has been on the rise among the smart grid power market network in electric power demand management based on the Internet. Open Automated Demand Response system protocol, which can deliver Demand Response needed in electric power demand management to electricity supplier, system supplier and even the user is openADR 2.0b. This paper used the most credible, cosmopolitanly proliferated EPRI open source and analysed the variety of security vulnerability that developed VEN and VTN system may have. Using the simulator for attacking openADR protocol, the VEN/VTN system that has been implemented as EPRI open source was conducted to attack in a variety of ways. As a result of the analysis, we were able to get the results that the VEN/VTN system has security vulnerabilities to the parameter tampering attacks and service flow falsification attack. In conclusion, if you want to implement the openADR2.0b protocol system in the open or two-way communication environment smart grid network, considering a variety of security vulnerability should be sure to seek security technology and services.

Thermal comfort during temperature cycles induced by direct load control strategies of peak electricity demand management

Direct load control (DLC) is a utility-sponsored demand response program which allows a utility to cycle specific appliances on and off during peak demand periods. Direct load control of air conditioners induces temperature cycles that might potentially compromise occupants' thermal comfort. In two separate experiments, 56 subjects' thermal comfort was closely examined during 6 DLC conditions and 2 control conditions simulated in a climate chamber, representing typical DLC-induced thermal environments in university lecture theatres. Results show that half of the DLC conditions were clearly accepted by subjects. Multilevel linear modelling of thermal sensation demonstrates that operative temperature, vapour pressure and the rate of temperature change are the three most important predictors during DLC events. Multilevel logistic regression indicates that in DLC conditions with lower adapting temperatures, thermal acceptability is significantly predicted by air speed and its interaction with operative temperature whereas in DLC conditions with higher adapting temperatures, by air speed, operative temperature and the rate of temperature change. Subjects' thermal comfort zone during DLC events is wider than predicted by Fanger's PMV/PPD model in that the former is more tolerant of cooler temperatures. Results from this study suggest that ASHRAE 55-2013 is overly conservative in defining the limits for temperature cycles, ramps and drifts.

不確実性下の戦略的電力需給マネジメント

不確実性下の戦略的電力需給マネジメント

Classification of Household Appliance Operation Cycles: A Case-Study Approach

In recent years, a new generation of power grid system, referred to as the Smart Grid, with an aim of managing electricity demand in a sustainable, reliable, and economical manner has emerged. With greater knowledge of operational characteristics of individual appliances, necessary automation control strategies can be developed in the Smart Grid to operate appliances in an efficient manner. This paper provides a way of classifying different operational cycles of a household appliance by introducing an unsupervised learning algorithm called k-means clustering. An intrinsic method known as silhouette coefficient was used to measure the classification quality. An identification process is also discussed in this paper to help users identify the operation mode each types of operation cycle stands for. A case study using a typical household refrigerator is presented to validate the proposed method. Results show that the proposed the classification and identification method can partition and identify different operation cycles adequately. Classification of operation cycles for such appliances is beneficial for Smart Grid as it provides a clear and convincing understanding of the operation modes for effective power management.

Single-Phase Multilevel PWM Inverter Based on H-bridge and its Harmonics Analysis

The efficient electric power demand management in electric power supply industry is currently being changed by distributed generation. Meanwhile, small-scale distributed generation systems using renewable energy are being constructed worldwide. Several small-scale renewable distributed generation systems, which can supply electricity to the grid at peak load of the grid as per policy such as demand response programs, could help in the stability of the electric power demand management. In this case, the power quality of the small-scale renewable distributed generation system is more significant. Low prices of power semiconductors and multilevel inverters with high power quality have been recently investigated. However, the conventional multilevel inverter topology is unsuitable for the small-scale renewable distributed generation system, because the number of devices of such topology increases with increasing output voltage level. In this paper, a single-phase multilevel inverter based on H-bridge, with DC_Link divided by bi-directional switches, is proposed. The proposed topology has almost half the number of devices of the conventional multilevel inverter topology when these inverters have the same output voltage level. Double Fourier series solution is mainly used when comparing PWM output harmonic components of various inverter topologies. Harmonic components of the proposed multilevel inverter, which have been analyzed by double Fourier series, are compared with those of the conventional multilevel inverter. An inverter prototype is then developed to verify the validity of the theoretical analysis.

Strategic Research on China Domestic Power System Modeling

Power system modeling is a software management tool for managing electricity demand, power system trading electricity and power system generation expansion planning purposes, through the combination of various models and their comparison, which can be used by the Government for its policy support and by the power enterprises for their business development planning decision support, and ensure that power enterprises can provide sufficient and safe quality power supplies at the lowest economic and environmental costs. Based on the study of existing power system models of advanced western countries as well as the current domestic power system modeling status, this article puts forward some proposals and ideas on building domestic power system models.

A Human Expert-Based Approach to Electrical Peak Demand Management

The objective of this paper is to propose a human expert-based approach to electrical peak demand management. The proposed approach helps to allocate demand curtailments (MW) among distribution substations (DS) or feeders in an electric utility service area based on requirements of the central load dispatch center. Demand curtailment allocation is quantified by taking into account demand response (DR) potential and load curtailment priority of each DS, which can be determined using DS loading level, capacity of each DS, customer types (residential/commercial), and load categories (deployable, interruptible, or critical). The analytic hierarchy process is used to model a complex decision-making process according to both expert inputs and objective parameters. Simulation case studies are conducted to demonstrate how the proposed approach can be implemented to perform DR using real-world data from an electric utility. Simulation results demonstrate that the proposed approach is capable of achieving realistic demand curtailment allocations among different DSs to meet the peak load reduction requirements at the utility level.

Modeling Demand Response Capability by Internet Data Centers Processing Batch Computing Jobs

Electricity cost has become a big concern of commercial cloud service providers with the rapid expansion of network-based cloud computing. Locationally, dispersed large-scale Internet data centers (IDCs) that underpin the cloud have increasing impact on the regional electricity market with their skyrocketing energy consumption. In this paper, the electricity market participation and accordingly the demand response capability of an IDC is defined as its temporally and spatially shiftable electricity demand quantities for processing delay-tolerant central processing unit-intense batch computing jobs. The demand response capability of the IDC is obtained by the proposed electric demand management solution. Price-sensitive and cooling efficiency-enabled batch computing workload dispatch with the objective of minimizing electricity cost is realized by dynamic IDC server consolidation and scheduling with virtual machine live migration technology. Numerical simulations show the effectiveness of the proposed demand management solution in IDC energy consumption and electricity cost reduction.

A survey on communication technologies and requirements for internet of electric vehicles

Electric vehicles (EVs) are becoming a more attractive transportation option, as they offer great cost savings, decrease foreign oil dependency, and reduce carbon emissions. However, varying temporal and spatial demand patterns of EVs threatens power grid operations and its physical components. Thus, the ability of the power grid to handle the potential extra load has become a major factor in the mainstream success. In order for this integration to occur seamlessly, the power grid and the consumers need to be coordinated in harmony. In this paper, we address the critical challenges introduced by the penetration of EVs, systematically categorize the proposed frameworks for demand management, and the role of information and communication technologies in the solution process. We provide a comprehensive survey on the communication requirements, the standards and the candidate technologies towards the Internet of electric vehicles (IoEV). This survey summarizes the current state of research efforts in electric vehicle demand management and aims to shed light on the continued studies.