Demand Response (DR) in residential sector is considered to play a key role in the smart grid framework because of its disproportionate amount of peak energy use and massive integration of distributed local renewable energy generation in conjunction with battery storage devices. In this paper, first a quick overview about residential demand response and its optimization model at single home and multi-home level is presented. Then a description of state-of-the-art optimization methods addressing different aspects of residential DR algorithms such as optimization of schedules for local RE based generation dispatch, battery storage utilization and appliances consumption by considering both cost and comfort, parameters uncertainty modeling, physical based dynamic consumption modeling of various appliances power consumption at single home and aggregated homes/community level are presented. The key issues along with their challenges and opportunities for residential demand response implementation and further research directions are highlighted.

Demand response on the residential market is becoming a solution to adapt customer consumption to the offer available and therefore lower the electricity peak prices. Tariff incentives and direct load control of residential air-conditioners and electric heaters are flexible solutions to reduce the peak demand. To include residential demand response resources in planning operators, quantifying the demand reduction is becoming a major issue for all electrical stakeholders. Current methods are based on day or weather matching, regressions and control group approaches. In general, methods using available data from a control group give more accurate results. With the introduction of smart meters, the electric utilities generate a large amount of quality data, available almost in real time. In this paper, we suggest using these available residential load curves to select a control group based on individual load curves. One of the advantages of our method is that the selected control group could adapt at anytime to the number of individuals belonging to the demand reduction program, as this number evolves with customers entering and leaving the program. Constrained regression methods and an algorithm are developed and evaluated on real data, providing a reliable solution for an operational use.

This paper presents the modeling and control for a novel Compressed Air Energy Storage (CAES) system for wind turbines. The system captures excess power prior to electricity generation so that electrical components can be downsized for demand instead of supply. Energy is stored in a high pressure dual chamber liquid-compressed air storage vessel. It takes advantage of the power density of hydraulics and the energy density of pneumatics in the “open accumulator” architecture. A liquid piston air compressor/expander is utilized to achieve near-isothermal compression/expansion for efficient operation. A cycle-average approach is used to model the dynamics of each component in the combined wind turbine and storage system. Standard torque control is used to capture the maximum power from wind through a hydraulic pump attached to the turbine rotor in the nacelle. To achieve both accumulator pressure regulation and generator power tracking, a nonlinear controller is designed based on an energy based Lyapunov function. The nonlinear controller is then modified to distribute the control effort between the hydraulic and pneumatic elements based on their bandwidth capabilities. As a result, liquid piston air compressor/expander will loosely maintain the accumulator pressure ratio, while the down-tower hydraulic pump/motor precisely tracks the desired generator power. This control scheme also allows the accumulator to function as a damper for the storage system by absorbing power disturbances from the hydraulic path generated by the wind gusts. A set of simulation case studies demonstrate the operation of the combined system when the nonlinear controller is utilized and illustrates how this system can be used for load leveling, downsizing electrical system and maximizing revenues.

This paper is concerned with maximum efficiency or power tracking for pneumatically-driven electric generator of a stand-alone small scale compressed air energy storage system (CAES). In this system, an air motor is used to drive a permanent magnet DC generator, whose output power is controlled by a buck converter supplying a resistive load. The output power of the buck converter is controlled power such that the air motor operates at a speed corresponding to either maximum power or maximum efficiency. The maximum point tracking controller uses a linearised model of the air motor together with integral control action. The analysis and design of the controller is based on a small injected-absorbed current signal-model of the buck converter. The controller was implemented experimentally using a dSPACE system. Test results are presented to validate the design and demonstrate its capabilities.

In this paper a new concept for control and performance assessment of compressed air energy storage (CAES) systems in a hybrid energy system is introduced. The proposed criterion, based on the concept of energy harvest index (HEI), measures the capability of a storage system to capture renewable energy. The overall efficiency of the CAES system and optimum control and design from the technical and economic point of view is presented. A possible application of this idea is an isolated community with significant wind energy resource. A case study reveals the usefulness of the proposed criterion in design, control and implementation of a small CAES system in a hybrid power system (HPM) for an isolated community. Energy harvested index and its effectiveness in increasing the wind penetration rate in the total energy production is discussed.

Distributed energy storage has been recognized as a valuable and often indispensable complement to small-scale power generation based on renewable energy sources. Small- scale energy storage positioned at the demand side would open the possibility for enhanced predictability of power output and easier integration of small-scale intermittent generators into functioning electricity markets, as well as offering inherent peak shaving abilities for mitigating contingencies and blackouts, for reducing transmission losses in local networks, profit optimization and generally allowing tighter utility control on renewable energy generation. Distributed energy storage at affordable costs and of low environmental footprint is a necessary prerequisite for the wider deployment of renewable energy and its deeper penetration into local networks.

Thermodynamic energy storage in the form of compressed air is an alternative to electrochemical energy storage in batteries and has been evaluated in various studies and tested commercially on a large scale. Small-scale distributed compressed air energy storage (DCAES) systems in combination with renewable energy generators installed at residential homes or small businesses are a viable alternative to large-scale energy storage, moreover promising lower specific investment than batteries. Flexible control methods can be applied to DCAES units, resulting in a complex system running either independently for home power supply, or as a unified and centrally controlled utility-scale energy storage entity.

This study aims at conceptualizing the plausible distributed compressed-air energy storage units, examining the feasibility for their practical implementation and analyzing their behavior, as well as devising the possible control strategies for optimal utilization of grid-integrated renewable energy sources at small scales. Results show that overall energy storage efficiency of around 70% can be achieved with comparatively simple solutions, offering less technical challenges and lower specific costs than comparable electrical battery systems. Furthermore, smart load management for improving the dispatchability can bring additional benefits by profit optimization and decrease.

Future energy systems will depend much more on renewable energy resources than the current ones. Renewable energy resources, in turn, fluctuate and are not permanently available to the same extent than fossil ones. In consequence, new approaches are required to balance electricity demand and production. One approach is to schedule the compressed-air production of industrial installations according to the current load and supply of the electric grid. To be able to do this, compressed-air has to be stored for peak load phases. Computer simulations are an efficient tool to judge the technical feasibility of such an approach and to compare it with other load management systems. This paper describes the thermodynamic fundamentals of compressed-air energy storage and their integration in a computer model. The obtained results from simulations were compared with results from measurements showing good consistency. Thus, the model was used to simulate different principles to store compressed-air. Systems with low pressure level and with high storage volume appear to be the most energy-efficient ones. In general the technology has the potential to be utilized in the electric load management. However, further simulations are required to determine the most economical solution.

The presence of energy hubs and the advancement in smart grid technologies have motivated system planners to deploy intelligent multicarrier energy systems entitled “smart energy hub” (S.E. Hub). In this paper, we model S.E. Hub, and propose a modern energy management technique in electricity and natural gas networks based on integrated demand side management (IDSM). In conventional studies, energy consumption is optimized from the perspective of each individual user without considering the interactions with each other. Here, the interaction among S.E. Hubs in IDSM program is formulated as a noncooperative game. The existence and uniqueness of a pure strategy Nash equilibrium (NE) is proved. Additionally, the strategies for each S.E. Hub are determined by proposing a distributed algorithm. We also address the IDSM game in a cloud computing (CC) framework to achieve efficient data processing and information management. Simulations are performed on a grid consisting of ten S.E. Hubs. We compare the CC framework with conventional data processing techniques to evaluate the efficiency of our proposed approach in determining NE. It is also shown that in the NE, the energy cost for each S.E. Hub and the peak-to-average ratio of the electricity demand decrease substantially.

Loesch, M.; Hufnagel, D. ; Steuer, S. ; Fabnacht, T. ; Schmeck, H.

A major component of the future Smart Grid is an adaptive demand side that allows to handle the fluctuating power supply based on renewable energies. In this paper, we present an evolutionary algorithm that allows for shifting electrical loads generated by heat pumps. Our approach is based on overheating the hot water storage in order to get a higher degree of freedom for scheduling. In our scenario, we assume time-variable price and load limitation signals as well as a prediction for local power generation from photovoltaic panels to incentivize the load shifting. Using these signals, we consider the future thermal demand to schedule the heat pump such that electricity costs are decreased. Our simulations show that heat pumps and hot water storages bear potential to shift loads over a time span of up to multiple hours, thus providing economical storage capacity. In doing so and based on electricity prices from the stock exchange, we were able to significantly decrease electricity costs for operating the heat pump.

Published in: Intelligent Energy and Power Systems (IEPS), 2014 IEEE International Conference on

Cecati, C. ; Citro, C. ; Siano, P.

The integration of renewable energy systems (RESs) in smart grids (SGs) is a challenging task, mainly due to the intermittent and unpredictable nature of the sources, typically wind or sun. Another issue concerns the way to support the consumers' participation in the electricity market aiming at minimizing the costs of the global energy consumption. This paper proposes an energy management system (EMS) aiming at optimizing the SG's operation. The EMS behaves as a sort of aggregator of distributed energy resources allowing the SG to participate in the open market. By integrating demand side management (DSM) and active management schemes (AMS), it allows a better exploitation of renewable energy sources and a reduction of the customers' energy consumption costs with both economic and environmental benefits. It can also improve the grid resilience and flexibility through the active participation of distribution system operators (DSOs) and electricity supply/demand that, according to their preferences and costs, respond to real-time price signals using market processes. The efficiency of the proposed EMS is verified on a 23-bus 11-kV distribution network.

Sustainable Energy, IEEE Transactions on (Volume:2 , Issue: 4 ) - 2001