Deferrable Loads Research Articles

Robust and Optimal Consumption Policies for Deadline-Constrained Deferrable Loads

The paper analyzes the optimal response of an individual smart load with deferrable demand for electricity, to exogenous and stochastic price processes. It is assumed that a smart load can delay a time-flexible energy demand up to a fixed deadline. Under mild assumptions on the regularity of the stochastic price process, it is shown that the optimal strategy is to consume only when the price is less than or equal to a certain threshold that depends only on the time left to the deadline and the price statistics. This analysis is performed under both perfect and partial information about the statistics of the price process. Robust policies with performance guarantees are derived for the partial information case. Such performance bounds are also used for deriving upper and lower bounds on the economic value of load-shifting. Numerical simulation results based on price data from wholesale electricity markets suggest that when information must be empirically extracted from data, the robust policies provide various theoretical and practical advantages over the full information policies.

Deferrable load scheduling under imperfect data communication channel

AbstractIn smart grid, the real‐time pricing is implemented to motivate power consumers to change their consumption profile dynamically. With the real‐time pricing, a deferrable load can be scheduled by its scheduler optimally so that the power consumption cost will be minimized. However, when the data communication in smart grid suffers from interference, congestion, malfunction in devices, or even cyber attack, it is possible that the power price information cannot be transmitted successfully to the scheduler. As a result, the scheduling performance will be negatively affected by the suboptimal decision‐making because of incomplete power price information. To overcome this problem, a partially observable Markov decision process based deferrable load scheduling algorithm is proposed. Besides, the implementation of a standby alternative channel with the purpose to improve the reliability of the data communication in smart grid is also discussed in this paper. The numerical results show that the proposed partially observable Markov decision process based algorithm and the implementation of standby channel can effectively improve the scheduling performance when the scheduler lacks actual price information. Copyright © 2014 John Wiley & Sons, Ltd.

Real-time deferrable load control

Real-time demand response is potential to handle the uncertainties of renewable generation. It is expected that a large number of deferrable loads, including electric vehicles and smart appliances, will participate in demand response in the future. In this paper, we propose a decentralized algorithm that reduces the tracking error between demand and generation, by shifting the power consumption of deferrable loads to match the generation in real-time. At each time step within the control window, the algorithm minimizes the expected tracking error to go with updated predictions on demand and generation. It is proved that the root mean square tracking error vanishes as control window expands, even in the presence of prediction errors.

Large-Scale Integration of Deferrable Demand and Renewable Energy Sources

We present a stochastic unit commitment model for assessing the impacts of the large-scale integration of renewable energy sources and deferrable demand in power systems in terms of reserve requirements. We analyze three demand response paradigms for assessing the benefits of demand flexibility: the centralized co-optimization of generation and demand by the system operator, demand bids and the coupling of renewable resources with deferrable loads. We motivate coupling as an alternative for overcoming the drawbacks of the two alternative demand response options and we present a dynamic programming algorithm for coordinating deferrable demand with renewable supply. We present simulation results for a model of the Western Electricity Coordinating Council.

Combined optimization of a wind farm and a well field for wind-enabled groundwater production

Energy requirements constitute a significant cost in groundwater production and can also add to a large carbon footprint when fossil fuels are used for power. Wind-enabled water production is advantageous as it minimizes air pollution impacts associated with groundwater production and relies on a renewable resource. Also, as groundwater extraction represents a deferrable load (i.e., it can be carried out when energy demands within an area are low), it provides a convenient way to overcome the intermittency issue associated with wind power production. Multiple turbine wind farms are needed to generate large quantities of power needed for large-scale groundwater production. Turbines must be optimally located in these farms to ensure proper propagation of kinetic energy throughout the system. By the same token, well placement must reconcile the competing objectives of minimizing interferences between production wells while ensuring the drawdowns at the property boundary are within acceptable limits. A combined simulation–optimization based model is developed in this study to optimize the combined wind energy and water production systems. The wind farm layout optimization model is solved using a re-sampling strategy, while the well field configuration is obtained using the simulated annealing technique. The utility of the developed model is to study wind-enabled water production in San Patricio County, TX. Sensitivity analysis indicated that identifying optimal placement of turbines is vital to extract maximum wind power. The variability of the wind speeds has a critical impact on the amount of water that can be produced. Innovative technologies such as variable flow pumping devices and aquifer storage recovery must be used to smooth out wind variability. While total groundwater extraction is less sensitive to uncertainty in hydrogeological parameters, improper estimation of aquifer transmissivity and storage characteristics can affect the feasibility of wind-driven groundwater production.

Real-Time Scheduling of Distributed Resources

We develop and analyze real-time scheduling algorithms for coordinated aggregation of deferrable loads and storage. These distributed resources offer flexibility that can enable the integration of renewable generation by reducing reserve costs. We present three scheduling policies: earliest deadline first (EDF), least laxity first (LLF), and receding horizon control (RHC). We offer a novel cost metric for RHC-based scheduling that explicitly accounts for reserve costs. We study the performance of these algorithms in the metrics of reserve energy and capacity through simulation studies. We conclude that the benefits of coordinated aggregation can be realized from modest levels of both deferrable load participation and flexibility.

Modeling and Stochastic Control for Home Energy Management

The problem of modeling and stochastic optimization for home energy management is considered. Several different types of load classes are discussed, including heating, ventilation, and air conditioning unit, plug-in hybrid electric vehicle, and deferrable loads such as washer and dryer. A first-order thermal dynamic model is extracted and validated using real measurements collected over an eight months time span. A mixed integer multi-time scale stochastic optimization is formulated for the scheduling of loads of different characteristics. A model predictive control based heuristic is proposed. Numerical simulations coupled with real data measurements are used for performance evaluation and comparison studies.

Real-Time Power Balancing Via Decentralized Coordinated Home Energy Scheduling

It is anticipated that an uncoordinated operation of individual home energy management (HEM) systems in a neighborhood would have a rebound effect on the aggregate demand profile. To address this issue, this paper proposes a coordinated home energy management (CoHEM) architecture in which distributed HEM units collaborate with each other in order to keep the demand and supply balanced in their neighborhood. Assuming the energy requests by customers are random in time, we formulate the proposed CoHEM design as a multi-stage stochastic optimization problem. We propose novel models to describe the deferrable appliance load (e.g., Plug-in (Hybrid) Electric Vehicles (PHEV)), and apply approximation and decomposition techniques to handle the considered design problem in a decentralized fashion. The developed decentralized CoHEM algorithm allow the customers to locally compute their scheduling solutions using domestic user information and with message exchange between their neighbors only. Extensive simulation results demonstrate that the proposed CoHEM architecture can effectively improve real-time power balancing. Extensions to joint power procurement and real-time CoHEM scheduling are also presented.

NPlug: An Autonomous Peak Load Controller

The Indian electricity sector, despite having the world's fifth largest installed capacity, suffers from a 12.9% peaking shortage. This shortage could be alleviated, if a large number of deferrable loads, particularly the high powered ones, could be moved from on-peak to off-peak times. However, conventional Demand Side Management (DSM) strategies may not be suitable for India as the local conditions usually favor inexpensive solutions with minimal dependence on the pre-existing infrastructure. In this work, we present a completely autonomous DSM controller called the nPlug. nPlug is positioned between the wall socket and deferrable load(s) such as water heaters, washing machines, and electric vehicles. nPlugs combine local sensing and analytics to infer peak periods as well as supply-demand imbalance conditions. They schedule attached appliances in a decentralized manner to alleviate peaks whenever possible without violating the requirements of consumers. nPlugs do not require any manual intervention by the end consumer nor any communication infrastructure nor any enhancements to the appliances or the power grids. Some of nPlug's capabilities are demonstrated using experiments on a combination of synthetic and real data collected from plug-level energy monitors. Our results indicate that nPlug can be an effective and inexpensive technology to address the peaking shortage. This technology could potentially be integrated into millions of future deferrable loads: appliances, electric vehicle (EV) chargers, heat pumps, water heaters, etc.

Optimizations of Power Consumption and Supply in the Smart Grid: Analysis of the Impact of Data Communication Reliability

Data communications infrastructure will play an important role to transfer various information in smart grid. In this paper, we consider the reliability of the smart grid data communications infrastructure and its impact on the power consumption and supply optimizations. For optimizing the power consumption, we consider a deferrable load scheduling method which is modeled by using a constrained Markov decision process (CMDP) model, taking into account the unavailability of the home area network (HAN) and neighborhood area network (NAN) gateways. For optimizing the power supply, we consider an economic dispatch method which is modeled by using stochastic programming (SP), taking into account the unavailability of the exact power demand and supply information. The power consumption and supply costs are analyzed. In addition, we show how these costs can be reduced through the deployment of component redundancy in the smart grid data communications infrastructure.

Seawater reverse osmosis (SWRO) as deferrable load in micro grids

Seawater desalination in the capacity range 100–1000 m3/day is globally of increasing importance, especially when it comes to in remote costal regions. By meeting the energy demand of desalination plants by using renewable energy technologies, the environmental impact can be minimised. For water production of more than a few cubic meters per day, usually conventional constantly operating desalination systems are used requiring the implementation of expensive energy storage systems and/or a grid-connection. Variable operating SWRO plants could eventually increase the efficiency of an energy supply system by minimising the otherwise unavoidable dump load and the capacity of energy storage systems. To provide evidence for this thesis, data of the Cape Verdean Island Brava were used and three main scenarios compared. Simulations using hourly data-sets show that energy supply systems with a high wind share can benefit from deferrable loads like a variable desalination plant. Based on their flexibility, such processes are very attractive to implement as dynamic load in stochastically fluctuating renewable energy supply systems. Technological requirements for a variable operation of a SWRO plant are described, considering pressure changes and interruptions of the energy supply and water flow. For the simulation, a preliminary plant design configured by SYNLIFT Systems is used. A detailed analysis of levelised costs of water and electricity highlights options, how desalination could be applied as deferrable load in grid-connected systems in a technologically feasible and economically profitable way.

Seawater desalination in micro grids: an integrated planning approach

Islands often depend on the import of fossil fuels for power generation. Due to the combined effect of high oil prices and transportation costs, energy supply systems based on renewable energies are already able to compete successfully with fossil fuel systems for a number of these islands. Depending on local and regional conditions, not only energy supply is a challenge, but also the finding of a reliable supply of water. A promising alternative to freshwater shipments is seawater desalination. Desalination processes can act as a flexible load whenever excess electricity generated by renewable sources is present. Numerical simulations of combined energy and water supply systems for the Caribbean island Petite Martinique, Grenada, are accomplished. Considering renewable energy sources like wind and solar radiation, energy storage technologies, and desalination processes, various scenarios are introduced and simulated, and the results are compared. An extension of the current energy supply system with renewable energy technologies reduces power generation costs by approximately 40%. The excess energy generated by renewables can supply a significant share of a desalination plant’s energy demand. The levelized costs of electricity and water show that the integration of desalination as a deferrable load is beneficial to the considered micro grid. The implementation of renewable energy generation and desalination as deferrable load is recommendable in Petite Martinique. Possible refinancing strategies depending on the combination of different electricity and water tariffs can be derived and applied to similar business cases in remote regions.

From Packet to Power Switching: Digital Direct Load Scheduling

At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.

An Optimal Stand-Alone Biomass/Solar-PV/Pico-Hydel Hybrid Energy System for Remote Rural Area Electrification of Isolated Village in Western-Ghats Region of India

Biomass based hybrid energy system is utilized for the electrification of villages, especially in developing countries like India. Hybrid Energy System (HES) components, feasibility study, and cost analysis are presented in this paper for a remote area Kakkavayal, a forest region in Kerala, India. A water stream at 25 m height has been identified at Kakkavayal by the Forest Department of Kerala. The village has been marked to study hourly measured meteorological and load data for a period of time. The performance of the proposed hybrid system is determined on hourly basis and optimum configuration, which can meet the energy demand with minimum cost using the hybrid system design tool HOMER. Parametric analysis indicates that with 2 kW solar PV, 15 kW pico-hydel, and 5 kW biomass gasifier generator together with five numbers of 12 V, 200 Ah of battery storage to meet the primary load demand of 56 kWh/d and 17 kWh/d (scaled annual average) of deferrable load. From the simulation, the cost of energy is found to be Rs 7.274 (US$ 0.164) per kWhr. The cost of energy of the proposed HES is also compared with the diesel-based HES. It is found that economically, the proposed HES is a very good alternative for present pico-hydel/diesel system.

Integration of Contracted Renewable Energy and Spot Market Supply to Serve Flexible Loads

AbstractWe present a contract for integrating renewable energy supply and electricity spot markets for serving deferrable electric loads in order to mitigate renewable energy intermittency. The contract which we describe results in a stochastic optimal control problem for minimizing the cost of serving flexible load. We solve the optimal control problem by using a recombinant lattice for modeling renewable power supply and electricity spot price uncertainty. We compare various control policies, and we analyze the sensitivity of our results with respect to various problem parameters.

H2RES, Energy planning tool for island energy systems – The case of the Island of Mljet☆

When it comes to the energy planning, computer programs like H2RES are becoming valuable tools. H2RES has been designed as support for simulation of different scenarios devised by RenewIsland methodology with specific purpose to increase integration of renewable sources and hydrogen into island energy systems. The model can use wind, solar, hydro, biomass, geothermal as renewable energy sources and fossil fuel blocks and grid connection with mainland as back up. The load in the model can be represented by hourly and deferrable electricity loads of the power system, by hourly heat load, by hydrogen load for transport and by water load depending on water consumption. The H2RES model also has ability to integrate different storages into island energy system in order to increase the penetration of intermittent renewable energy sources or to achieve a 100% renewable island. Energy storages could vary from hydrogen loop (fuel cell, electrolyser and hydrogen storage) to reversible hydro or batteries for smaller energy systems. The H2RES model was tested on the power system of the Island of Porto Santo – Madeira, the islands of Corvo, Graciosa, and Terrciera – Azores, Sal Island – Cape Verde, Portugal, the Island of Mljet, Croatia and on the energy system of the Malta. Beside energy planning of the islands, H2RES model could be successfully applied for simulation of other energy systems like villages in mountain regions or for simulation of different individual energy producers or consumers.

Design, economic analysis and environmental considerations of mini-grid hybrid power system with reverse osmosis desalination plant for remote areas

This paper discusses the design process of a mini-grid hybrid power system with reverse osmosis desalination plant for remote areas, together with an economic analysis and environmental considerations for the project life cycle. It presents a design scenario for supplying electricity and fulfilling demand for clean water in remote areas by utilising renewable energy sources and a diesel generator with a reverse osmosis desalination plant as a deferrable load. The economic issues analysed are the initial capital cost needed, the fuel consumption and annual cost, the total net present cost (NPC), the cost of electricity (COE) generated by the system per kWh and the simple payback time (SPBT) for the project. The environmental considerations discussed are the amount of gas emissions, such as CO 2 and NO x , as well as particulate matter released into the atmosphere. Simulations based on an actual set of conditions in a remote area in the Maldives were performed using HOMER for two conditions: before and after the Tsunami of 26th December 2004. Experimental results on the prototype 5 kVA mini-grid inverter and reverse osmosis desalination plant, rated at 5.5 kWh/day, are also presented here to verify the idea of providing power and water supplies to remote areas.