This package provides a powerful simulation toolkit for thermal engineering plants such as power plants, district heating systems or heat pumps.

A top-like utility to monitor the sources of power consumption; allows to turn on/off many components; quite useful to track possible power-related issues.

Most of the talk about renewable energy is aimed at electricity production. However, most of the energy we need is heat, which solar panels and wind turbines cannot produce efficiently. To power industrial processes like the making of chemicals, the smelting of metals or the production of microchips, we need a renewable source of thermal energy. Direct use of solar energy can be the solution, and it creates the possibility to produce renewable energy plants using only renewable energy plants, paving the way for a truly sustainable industrial civilization.

The deployment of solar-based electricity generation, especially in the form of photovoltaics (PVs), has increased markedly in recent years due to a wide range of factors including concerns over greenhouse gas emissions, supportive government policies, and lower equipment costs. Still, a number of challenges remain for reliable, efficient integration of solar energy. Chief among them will be developing new tools and practices that manage the variability and uncertainty of solar power.

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.

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.

The need for fast response demand side participation (DSP) has never been greater due to increased wind power penetration. White domestic goods suppliers are currently developing a ‘smart’ chip for a range of domestic appliances (e.g. refrigeration units, tumble dryers and storage heaters) to support the home as a DSP unit in future power systems. This paper presents an aggregated population-based model of a single compressor fridge-freezer. Two scenarios (i.e. energy efficiency class and size) for valley filling and peak shaving are examined to quantify and value DSP savings in 2020. The analysis shows potential peak reductions of 40 MW to 55 MW are achievable in the Single wholesale Electricity Market of Ireland (i.e. the test system), and valley demand increases of up to 30 MW. The study also shows the importance of the control strategy start time and the staggering of the devices to obtain the desired filling or shaving effect.

Plug-loads are often neglected in commercial demand response (DR) despite being a major contributor to building energy consumption. Improvements in technology like smart power strips are prompting the incorporation of plug-loads as a DR resource alongside building HVAC and lighting. Office scale battery storage (OSBS) systems are also candidates as a DR resource due to their ability to run on battery power. In this work, we present a model predictive control (MPC) framework for optimal load-shedding of plug-loads and OSBS.We begin with discussion of the context of this work, and present two models of OSBS systems. A model predictive controller for OSBS and plug-load load-shed scheduling is presented. We discuss casting the MPC as a dynamic program, and an algorithm to solve the dynamic program. Simulation results show the efficacy and utility of dynamic programming, and quantify the performance of OSBS systems.

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.

@ARTICLE{7160638,
author={Shi, X. and Li, Y. and Cao, Y. and Tan, Y.},
journal={Power and Energy Systems, CSEE Journal of},
title={Cyber-physical electrical energy systems: challenges and issues},
year={2015},
month={June},
volume={1},
number={2},
pages={36-42},
abstract={Cyber-physical electrical energy systems (CPEES) combine computation, communication and control technologies with physical power system, and realize the efficient fusion of power, information and control. This paper summarizes and analyzes related critical scientific problems and technologies, which are needed to be addressed with the development of CPEES. Firstly, since the co-simulation is an effective method to investigate infrastructure interdependencies, the co-simulation platform establishment of CPEES and its evaluation is overviewed. Then, a critical problem of CPEES is the interaction between energy and information flow, especially the influence of failures happening in information communication technology (ICT) on power system. In order to figure it out, the interaction is analyzed and the current analysis methods are summarized. For the solution of power system control and protection in information network environment, this paper outlines different control principles and illustrates the concept of distributed coordination control. Moreover, mass data processing and cluster analysis, architecture of communication network, information transmission technology and security of CPEES are summarized and analyzed. By solving the above problems and technologies, the development of CPEES will be significantly promoted.},
keywords={Cyber-physical electrical energy systems;informationcommunication technology;power system;smart grid},
doi={10.17775/CSEEJPES.2015.00017},}

@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)

@INPROCEEDINGS{7174716,
author={Powroznik, Piotr and Michta, Emil},
booktitle={Nonsinusoidal Currents and Compensation (ISNCC), 2015 International School on},
title={Elastic model of energy management in micro smart grid},
year={2015},
month={June},
pages={1-6},
abstract={In modern electric power systems (EPS) solutions based on advanced metering infrastructure (AMI) are required. The data collected by AMI could help to detect an emergency situations in EPS. AMI two way communications allows both to collect measuring data and control devices referred to as a nodes. Nodes providing control and measurement functions create a smart grid (SG). The smart grid nodes are appliances, power generation and storage capacity. In the case of power storage, these nodes can also take power, in the case of having to be recharged. Analysis of all possible power combining, for all three categories of nodes in even micro SG (MSG) may not be feasible due to time. A novel approach to the power management in MSG by use of an elastic management energy model have been presented in the paper.},
keywords={Artificial intelligence;Heating;Reliability;Security;Smart grids;balanced power selection;elastic management energy model;micro smart grids},
doi={10.1109/ISNCC.2015.7174716},}

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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.