Showing papers on "Load shifting published in 2012"

Demand Side Management in Smart Grid Using Heuristic Optimization

Abstract: Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

Application of Model Predictive Control for Active Load Management in a Distributed Power System With High Wind Penetration

Abstract: This paper introduces an experimental platform (SYSLAB) for the research on advanced control and power system communication in distributed power systems and one of its components-an intelligent office building (PowerFlexHouse), which is used to investigate the technical potential for active load management. It also presents in detail how to implement a thermal model predictive controller (MPC) for the heaters' power consumption prediction in the PowerFlexHouse. It demonstrates that this MPC strategy can realize load shifting, and using good predictions in MPC-based control, a better matching of demand and supply can be achieved. With this demand side control study, it is expected that MPC strategy for active load management can dramatically raise energy efficiency and improve grid reliability, when there is a high penetration of intermittent energy resources in the power system.

Effects of Load Sector Demand Side Management Applications in Generating Capacity Adequacy Assessment

Abstract: Electric power utilities are facing increasing infrastructure investment and awareness of environmental issues. Demand side management (DSM) can play an important role in meeting some of the challenges faced by electric power utilities in that it can be used to improve energy efficiency, system reliability, and security, reduce costs for both customers and utilities, and mitigate environmental damage. This paper applies load shifting on seven different customer load sectors and illustrates the effects of the various DSM measures on the load shapes and on the system reliability indices used in generating capacity adequacy assessment.

Demand Response Architectures and Load Management Algorithms for Energy-Efficient Power Grids: A Survey

Abstract: A power grid has four segments: generation, transmission, distribution and demand Until now, utilities have been focusing on streamlining their generation, transmission and distribution operations for energy efficiency While loads have traditionally been a passive part of a grid, with rapid advances in ICT, demand-side technologies now play an increasingly important role in the energy efficiency of power grids This paper starts by introducing the key concepts of demand-side management and demand-side load management Classical demand-side management defines six load shape objectives, of which "peak clipping" and "load shifting" are most widely applicable and most relevant to energy efficiency At present, the predominant demand-side management activity is demand response (DR) This paper surveys DR architectures, which are ICT architectures for enabling DR programs as well as load management This paper also surveys load management solutions for responding to DR programs, in the form of load reduction and load shifting algorithms A taxonomy for "group load shifting" is proposed Research challenges and opportunities are identified and linked to ambient intelligence, wireless sensor networks, nonintrusive load monitoring, virtual power plants, etc

Model Predictive Controller for Active Demand Side Management with PV self-consumption in an intelligent building

Abstract: This paper presents a Model Predictive Controller (MPC) for electrical heaters' predictive power consumption including maximizing the use of local generation (e.g. solar power) in an intelligent building. The MPC is based on dynamic power price and weather forecast, considering users' comfort settings to meet an optimization objective such as minimum cost and minimum reference temperature error. It demonstrates that this MPC strategy can realize load shifting, and maximize the PV self-consumption in the residential sector. With this demand side control study, it is expected that MPC strategy for Active Demand Side Management (ADSM) can dramatically save energy and improve grid reliability, when there is a high penetration of Renewable Energy Sources (RESs) in the power system.

Battery energy storage system load shifting control based on real time load forecast and dynamic programming

Abstract: Battery energy storage system (BESS) is one of the key technologies for smart grid and load shifting is one of the fundamental functions of BESS. BESS load shifting performance is determined by the availability of accurate load curves and optimization approaches. In this paper, a real-time control strategy based on load forecast and dynamic programming methods is presented. The predicted load curve is updated on-line through regress forecasting. The proposed optimization model is solved by using dynamic programming technique. The objective is peak shaving and prolonging the battery lifetime, and the constraints considered include battery state-of-charge (SOC), cycling times per day, converter capacity and step power. The above control strategy was successfully applied to the 5MW*4hour lithium-Ion BESS demonstration project in Biling substation, China Southern Power Grid. The simulation results based on the actual load data of Biling substation demonstrate the effectiveness.

A model predictive control approach to the load shifting problem in a household equipped with an energy storage unit

Abstract: This paper deals with the load shifting problem in a household equipped with smart appliances and an energy storage unit with conversion losses. The problem is faced by establishing an event driven Model Predictive Control framework aiming to meet the real life dynamics of a household and to keep low the impact of the control system on the total electric energy consumption. The proposed approach allows the consumer to minimize the daily energy cost in scenarios characterized by Time of Use tariffs and Demand Side Management, by dynamically evaluating the best time to run of the appliances and the optimal evolution of the battery level of charge. A proper set of realistic simulations validates the proposed approach, showing the relevance of the energy storage unit in the domestic load shifting architecture.

Analysis of battery storage utilization for load shifting and peak smoothing on a distribution feeder in New Mexico

Abstract: A Smart Grid demonstration project is currently underway at Public Service Company of New Mexico (PNM). The project combines both residential and commercial loads on a dedicated feeder, with high PV penetration ratio, equipped with a 0.5MW substation-sited photovoltaic (PV) system and large-scale utility storage. The unique aspect of the battery storage system being used, is that both slow (load-shifting) and fast (intermittency-mitigation) power discharge modes are possible. Smart meters and customer Demand Response (DR) management, along with some customer-owned storage will all be implemented. This program targets a minimum of 15% peak-load reduction at a specific feeder through a combination of these the devices and measures. In this paper, we present goals, future plans and current results of modeling for the existing and future infrastructure of this Smart Grid project.

Energy Management for Fuel-Cell Hybrid Vehicles Based on Specific Fuel Consumption Due to Load Shifting

Abstract: Energy management is integral to the design of fuel-cell (FC) hybrid vehicles (FCHVs), as well as topology and component sizing. Therefore, an optimal energy management strategy is crucial for a successful FCHV concept. The goal in this optimization process is to coordinate the energy sources and power components on a vehicle in such a way that the total consumption is minimized. We defined a new strategy that quantifies the shifts in the operating point (OP) of the FC system by using the specific fuel consumption due to load shifting and adapts the conditions for a possible shifting of the OP of the FC system to reduce the total fuel consumption. This paper shows the simulation results of the proposed strategy in comparison with existing strategies. The main focus is given on the improvement in fuel consumption, the effect of the new approach on utilization of the power sources concerning their aging mechanisms, and the effect of varying conditions by time. The results indicate that the goal of reduction in total fuel consumption is achieved.

Optimization of peak-valley TOU power price time-period in ordered charging mode of electric vehicle

Abstract: This paper presents an optimized model for TOU power price time-period considering how to use ordered charging of electric vehicles (EV) to cut the peak and fill the valley of electricity power grid. First, a response model of charging time is presented, then the typical day load curve of EV ordered charging is obtained by Monte Carlo simulation, which is attached to the original load curve to form the real load of power grid. Based on this, an optimization model is built with the purpose of minimizing the peak-valley ratio. A genetic algorithm is used to solve this problem to get the best time-period for load shifting. The results of example show the rationality of this model and method.

Optimization of Energy Storage System Capacity for Relaxing Peak Load Regulation Bottlenecks

Abstract: Using large-scale battery energy storage systems for load shifting and peak smoothing can decrease the fluctuation of daily load and reduce load tracking regulation burden of generator units,and enlarge down-regulate space to accommodate wind power.Based on a given load characteristics,the relationship between the energy storage system allocation capacity and improvement of load fluctuations was analyzed.By making energy storage system’s investment costs and economic benefits as constraints,and by maximizing the comprehensive benefits as the object,an optimal capacity-allocation method was proposed for large-scale energy storage systems to relax peak-regulation bottleneck.For the limitation of wind power accommodation of a province in China in 2010,the method was applied to improve the situation and the impact of the life cycle and investment cost of the energy storage system on its capacity configuration.Case study results verified the feasibility of the proposed method.

Prospects for Renewable Energy: Meeting the Challenges of Integration with Storage

Abstract: Publisher Summary This chapter focuses on integration issues surrounding solar power since wind integration is covered in several other chapters. A discussion follows on the possible solutions to these issues using energy storage, which is applicable to the integration of both wind and solar resources. Key benefits of energy storage include providing balancing services (e.g., regulation and load following), which enable the widespread integration of renewable energy; supplying power during brief disturbances to reduce outages and the financial losses that accompany them; and serving as substitutes for transmission and distribution upgrades to defer or eliminate them. A smart grid is needed to maximize benefits from load shifting and ancillary services. To maximize these benefits and minimize costs, each energy storage technology needs to be optimized for certain applications. In particular, battery technologies are promising due to their wide range of chemistries and operating conditions for providing services that cover several applications.

A two level optimal DSM load shifting formulation using genetics algorithm case study: Residential loads

Abstract: Utilities around the world have been considering Demand Side Management (DSM) in their strategic planning. The costs of constructing and operating a new capacity generation unit are increasing everyday as well as Transmission and distribution and land issues for new generation plants, which force the utilities to search for another alternatives without any additional constraints on customers comfort level or quality of delivered product. De can be defined as the selection, planning, and implementation of measures intended to have an influence on the demand or customer-side of the electric meter, either caused directly or stimulated indirectly by the utility. DSM programs are peak clipping, Valley filling, Load shifting, Load building, energy conservation and flexible load shape. The main target of this paper is to show the impact of a DSM load shifting program on both utility and customer in residential areas by Maximizing load factor with minimizing area-gap-between load and supply from the utility point of view and minimizing the cost of energy consumption from the customer point of view with deriving the mathematical formulation of the objective function subjected to different constraints for different cases.

Learning from residential load data: Impacts on LV network planning and operation

Abstract: The deployment of advanced metering infrastructure has already started in many countries around the world in order to facilitate the transition towards low-carbon economies, to improve electricity billing, to decrease distribution network operational costs, and to empower householders. In addition, the adoption of photovoltaic panels, electric vehicles and smart appliances, already being encouraged by governments, will change the way households consume and generate electricity. However, in order to adequately assess the impacts from these low-carbon technologies it is required a much better understanding of how electricity is currently consumed. This work firstly studies the effects of load characterization on the optimal selection of the conductors from the planning perspective based on a high granularity model for UK residential consumers that mimics data that could eventually be available through smart meters. Then, from the operational point of view, the benefits of load shifting (i.e., demand side management) to reduce peak demand are also investigated. The latter study is applied to a real LV network the North West of England. Results clearly indicate the potential benefits on LV network planning from high granularity data, as well as the important insights that could be gained from modeling load shifting schemes using such a data.

Modeling demand response of consumers to incentives using fuzzy systems

Abstract: The requirements of distribution grids are increasing. In a smart grid, critical states and overstress are predictable. If any kind of load shedding is available then grid expansions can be alleviated. Demand response could be used to motivate consumers to load shifting via incentives. Potential of load shifting in households exists, but remains uncertain so far due to the lack of knowledge about responsiveness of consumers to different kinds of incentives even if this is the key issue for demand response programs. This paper introduces a completely new approach for a micro-economic model, which estimates the price responsiveness of consumers to incentives in a rational decision making model based on fuzzy technology. It allows investigating the effect of different kinds of incentives on the demand side participation and response, the impact on residential consumers and the consequences for the low voltage distribution grids.

Electric Vehicle Load Management

Abstract: A distributed and collaborative load balancing method is disclosed that uses a utility's existing transmission and distribution system to charge an Electric Vehicle (EV) using load shifting over time and minimizes the overall cost of energy usage to charge EVs. The collaborative load balancing ensures grid reliability.

Using Utility Load Data to Estimate Demand for Space Cooling and Potential for Shiftable Loads

Abstract: This paper describes a simple method to estimate hourly cooling demand from historical utility load data. It compares total hourly demand to demand on cool days and compares these estimates of total cooling demand to previous regional and national estimates. Load profiles generated from this method may be used to estimate the potential for aggregated demand response or load shifting via cold storage.

The potential of thermostatically controlled appliances for intra-hour energy storage applications

Abstract: This paper investigates the potential of providing a variety of energy storage services by directly control the thermostatically controlled appliances (TCAs) from a centralized controller. Dispatch algorithms for the controller to arrange the turn-on and turn-off time and duration of individual TCAs are presented. The control goal is to operate each TCA within the customer-desired temperature range and maintain the TCA load diversity, and make the aggregated TCA load at the target load level. Methods to minimize the communication needs by reducing the monitoring and control data flows between the central controller and the end devices are also discussed. A thousand space heating units are modeled to demonstrate the control algorithms to provide load shifting and load balancing services for a period of 24 hours. The results demonstrate that the energy and ancillary services provided by the TCA loads meet the performance requirements and can become a major source of revenue for load-serving entities where the two-way communication smart grid infrastructure enables direct load control over the TCA loads.

Electric vehicles as flexible loads: Algorithms to optimize aggregate behavior

Abstract: This paper considers load shifting for electric vehicles (EVs) to reduce the peak value of the total power consumption. Large numbers of EV charging requests are classified into relatively small number of load types so that the computational effort remains unchanged as number of loads increases. A twolayer optimization scheme is proposed to deal with tasks that may demand sequentially varying power levels, which further reduces the computational burden. These ideas are potentially useful in harnessing flexibility in electric loads.

Load Shifting by Heat Pumps using Thermal Storage

Abstract: This paper describes a distributed energy storage system to minimize the impact of heat pumps (HPs) in a typical Dutch low voltage (LV) grid during an extreme winter situation. The degree of flexibility of the storage to facilitate the electricity grid is determined throughout the year by considering the limitations of the network capacity. Thermal storage applications are integrated to control the HPs independently of the heat demands. Reinforcement of the LV-grid is avoidable. An average of 30% of the total electricity demand is shiftable. The limitations of LV-grid have only a large effect on the load shift during extreme cold weather conditions.

Demand-side approaches to manage electricity outages in developing countries

Abstract: This paper describes significant cost saving opportunities for consumers in developing countries by the use of computational intelligence and demand-side-management techniques to mitigate the massive use of diesel back-up during grid outages. Application of load scheduling optimization is investigated during scheduled power outages for statistical distributions of loads and diesel pricing for residential consumer in India under conditions of both a flat-rate and Time-of-Day grid tariff structure. Two load shifting approaches are explored - a casual load shift out of blackout region and a combined TOD-Outage scheduler. The load types modeled include passive loads and schedulable. Maximum executable peak load at any time is constrained by an aggregate load limit. The maximum diesel savings for consumer due to load shifting can be approximately 40% for a flat-tariff grid to more than 70% for a TOD-tariff grid. Effect of rising diesel prices on the economic benefits of load-shifting is also examined.

A Generation and Load Integrated Scheduling on Interaction Mode on Customer Side

Abstract: In view of the challenge by the system peak-valley difference due to the large-scale wind power anti-peak-regulation characteristic and air-conditioning load to both the economy and security of power system operation,a new interactive mode of voluntarily declaring power consumption willingness and peak-load shifting cost is designed.The interactive mode is based on an analysis of load shifting potential of commercial and industrial customers.Based on this mode,a load model of interactive customer fully reflecting customer willingness on the customer side is developed.A generation and load integrated scheduling model is built by embedding the load model of interactive customer into the traditional security constrained unit combined model.The load model of interactive customer can participate in power network operation as scheduling resources,reducing the cost of expensive generator start and stop as well as the operation cost of the power system.New England 39-bus system validates the effectiveness of the proposed mode and model.

Cost-benefit to the cement industry by shifting evening load to off-peak periods

Abstract: Cement manufacturing is an energy-intensive processwhereelectricity costs account for up to 15% of the total manufacturing costs. The increasing electricity tariffs in South Africamake it imperative for cement producers to implement or become involved in DSM initiatives in order to remain competitive. In this paper, a case study of a load shifting project that was implemented at a typical South African cement plant is presented. The focus is on an energy management system that schedules the operation of the mills to shift load from peak to off-peak periods. The operating schedules are calculated on the basis of a simulation that minimises the operating costs of the mills, based on Eskom's variable cost, time-of-use tariffstructure. The simulation takes into consideration various factors, such as scheduled maintenance, equipment reliability and silo storage capacities, to provide accurate operating schedules that maximise load shift potential without adversely affecting production targets. The simulation also predicts thelevels of the various silos, which is invaluable information for production planning. The results of the implementation of the load shift project, obtained over a period of three months, is provided as confirmation of the considerable cost-benefits that can be achieved by shifting evening load of the mills to off-peak periods.

A flexible Markov-chain model for simulating demand side management strategies with applications to distributed photovoltaics

Abstract: In this paper a stochastic model for load shifting was utilized for the purpose of investigating the potential for increased self-consumption of photovoltaic (PV) generation in households. We show ...

Energy aware building automation enables Smart Grid-friendly buildings

Abstract: Building automation is the key to exploiting flexibilities in building operation with regard to integration of renewable energy sources, load shifting and general optimization for energy efficiency. Based on thermal and electric models of building physics and the energy producers and consumers in the building it is possible to assess the current state of the building and its systems and make predictions about the expected future behavior, which can be exploited to be beneficial for the electric and thermal grid.

Demand Side Management potential of refrigerators with different energy classes

Abstract: Power systems of today have several problems such as growing demand and fluctuations in energy consumption. Demand Side Management as a subtopic of Smart Grid provides various solutions to these problems. Intelligent Load Control has a great potential for load shifting and savings if used together with thermostatic loads. Cooling systems, especially refrigerators, mostly have a big ratio in residential energy consumption. Refrigerators with close parameters, similar working environments and periodic working schedules can provide accurate estimations of energy consumption with simulations. In this study, DSM potential of two refrigerators with different energy efficiency classes is analyzed. By using a refrigerator model and the measurements collected from real devices, achievable percentage of demand change in peak times and savings in annual electricity bills are estimated. The study showed that with multiple pricing and load control, annual electricity costs of refrigerators can be reduced by 12.60 to 14.78%, while decreasing demand in peak periods by 23.12 to 41.73%.