This paper investigates heat pump systems in smart grids, focussing on fields of application and control approaches that have emerged in academic literature. Based on a review of published literature technical aspects of heat pump flexibility, fields of application and control approaches are structured and discussed. Three main categories of applications using heat pumps in a smart grid context have been identified: First stable and economic operation of power grids, second the integration of renewable energy sources and third operation under variable electricity prices. In all fields heat pumps - when controlled in an appropriate manner - can help easing the transition to a decentralized energy system accompanied by a higher share of prosumers and renewable energy sources. Predictive controls are successfully used in the majority of studies, often assuming idealized conditions. Topics for future research have been identified including: a transfer of control approaches from simulation to the field, a detailed techno-economic analysis of heat pump systems under smart grid operation, and the design of heat pump systems in order to increase flexibility are among the future research topics suggested.

In this paper, we consider multi-pursuer single-superior-evader pursuit-evasion differential games where the evader has a speed that is similar to or higher than the speed of each pursuer. A new fuzzy reinforcement learning algorithm is proposed in this work. The proposed algorithm uses the well-known Apollonius circle mechanism to define the capture region of the learning pursuer based on its location and the location of the superior evader. The proposed algorithm uses the Apollonius circle with a developed formation control approach in the tuning mechanism of the fuzzy logic controller (FLC) of the learning pursuer so that one or some of the learning pursuers can capture the superior evader. The formation control mechanism used by the proposed algorithm guarantees that the pursuers are distributed around the superior evader in order to avoid collision between pursuers. The formation control mechanism used by the proposed algorithm also makes the Apollonius circles of each two adjacent pursuers intersect or be at least tangent to each other so that the capture of the superior evader can occur. The proposed algorithm is a decentralized algorithm as no communication among the pursuers is required. The only information the proposed algorithm requires is the position and the speed of the superior evader. The proposed algorithm is used to learn different multi-pursuer single-superior-evader pursuit-evasion differential games. The simulation results show the effectiveness of the proposed algorithm.

In 2005, two scientists, David Mazières and Eddie Kohler, wrote a paper titled Get me off Your Fucking Mailing List and submitted it to WMSCI 2005 (the 9th World Multiconference on Systemics, Cybernetics and Informatics), a conference then notorious for its spamming and lax standards for paper acceptance, in protest of same. The paper consisted essentially only of the sentence "Get me off your fucking mailing list" repeated many times.

by Truong X. Nghiem, Rahul Mangharam

Peak power consumption is a universal problem across energy control systems in electrical grids, buildings, and industrial automation where the uncoordinated operation of multiple controllers result in temporally correlated electricity demand surges (or peaks). While there exist several different approaches to balance power consumption by load shifting and load shedding, they operate on coarse grained time scales and do not help in de-correlating energy sinks. The Energy System Scheduling Problem is particularly hard due to its binary control variables. Its complexity grows exponentially with the scale of the system, making it impossible to handle systems with more than a few variables.

We developed a scalable approach for fine-grained scheduling of energy control systems that novelly combines techniques from control theory and computer science. The original system with binary control variables are approximated by an averaged system whose inputs are the utilization values of the binary inputs within a given period. The error between the two systems can be bounded, which allows us to derive a safety constraint for the averaged system so that the original system's safety is guaranteed. To further reduce the complexity of the scheduling problem, we abstract the averaged system by a simple single-state single-input dynamical system whose control input is the upper-bound of the total demand of the system. This model abstraction is achieved by extending the concept of simulation relations between transition systems to allow for input constraints between the systems. We developed conditions to test for simulation relations as well as algorithms to compute such a model abstraction. As a consequence, we only need to solve a small linear program to compute an optimal bound of the total demand. The total demand is then broken down, by solving a linear program much smaller than the original program, to individual utilization values of the subsystems, whose actual schedule is then obtained by a low-level scheduling algorithm. Numerical simulations in Matlab show the effectiveness and scalability of our approach.

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.

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.

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.

@INPROCEEDINGS{7170976,
author={Risbeck, Michael J. and Maravelias, Christos T. and Rawlings, James B. and Turney, Robert D.},
booktitle={American Control Conference (ACC), 2015},
title={Cost optimization of combined building heating/cooling equipment via mixed-integer linear programming},
year={2015},
month={July},
pages={1689-1694},
abstract={In this paper, we propose a mixed-integer linear program to economically optimize equipment usage in a central heating/cooling plant subject to time-of-use and demand charges for utilities. The optimization makes both discrete on/off and continuous load decisions for equipment while determining utilization of thermal energy storage systems. This formulation allows simultaneous optimization of heating and cooling subsystems, which interact directly when heatrecovery chillers are present. Nonlinear equipment models are approximated as piecewise-linear to balance modeling accuracy with the computational constraints imposed by online implementation and to ensure global optimality for the computed solutions. The chief benefits of this formulation are its ability to tightly control on/off switching of equipment, its consideration of cost contributions from auxiliary equipment such as pumps, and its applicability to large systems with multiple heating and cooling units in which a combinatorial problem must be solved to pick the optimal mix of equipment. These features result in improved performance over heuristic scheduling rules or other formulations that do not consider discrete decision variables. We show optimization results for a system with four conventional chillers, two heat-recovery chillers, and one hot water boiler. With a timestep of 1 h and a horizon of 48 h, the optimization problem can be solved to optimality within 5 minutes, indicating suitability for online implementation.},
keywords={Biological system modeling;Cooling;Generators;Load modeling;Optimization;Production;Switches},
doi={10.1109/ACC.2015.7170976},}

Coordinated Electric Vehicle Charging Control with Aggregator Power Trading and Indirect Load Control
James J.Q. Yu, Junhao Lin, Albert Y.S. Lam, Victor O.K. Li
(Submitted on 4 Aug 2015)

Due to the increasing concern on greenhouse gas emmissions and fossil fuel security, Electric Vehicles (EVs) have attracted much attention in recent years. However, the increasing popularity of EVs may cause stability issues to the power grid if their charging behaviors are uncoordinated. In order to address this problem, we propose a novel coordinated strategy for large-scale EV charging. We formulate the energy trade among aggregators with locational marginal pricing to maximize the aggregator profits and to indirectly control the loads to reduce power network congestion. We first develop a centralized iterative charging strategy, and then present a distributed optimization-based heuristic to overcome the high computational complexity and user privacy issues. To evaluate our proposed approach, a modified IEEE 118 bus testing system is employed with 10 aggregators serving 30 000 EVs. The simulation results indicate that our proposed approach can effectively increase the total profit of aggregators, and enhance the power grid stability. 

Subjects: Systems and Control (cs.SY)