Intelligent Residential Air-Conditioning System With Smart-Grid Functionality

This paper sets forth a novel intelligent residential air-conditioning (A/C) system controller that has smart grid functionality. The qualifier “intelligent” means the A/C system has advanced computational capabilities and uses an array of environmental and occupancy parameters in order to provide optimal intertemporal comfort/cost trade-offs for the resident, conditional on anticipated retail energy prices. The term “smart-grid functionality” means that retail energy prices can depend on wholesale energy prices. Simulation studies are used to demonstrate the capabilities of the proposed A/C system controller

Development of an agent-based distribution test feeder with smart-grid functionality

This paper reports on the development of an agent-based distribution test feeder with smart-grid functionality. The test feeder is based on an actual distribution feeder with various additional features incorporated, including rooftop photovoltaic generation and price-responsive loads (e.g., plug-in electric vehicles and intelligent air-conditioning systems). This work aims to enable the integrated study of wholesale electric power markets coupled with detailed representations of the retail-side distribution systems

Model Predictive Control Of Inverter Air Conditioners Responding to Real-Time Electricity Prices In Smart Grids

Real-time electricity prices provide great opportunities for residential consumers to participate in demand response (DR) programs in smart grids. One of the biggest challenges faced by the DR program participants are the lack of advanced DR-enabled residential appliances which can automatically respond to real-time electricity prices, especially the lack of DR-enabled air conditioners which are the major contributors to electricity bills. In this paper, we aim to use model predictive control (MPC) techniques to control the inverter air conditioners. Instead of simply adjusting temperature set-points in the conventional DR control strategies, the MPC controllers are used to directly control the operating frequencies of inverter ACs. In contrast to the conventional control methods for inverter-driven ACs, the MPC approach can systematically integrate the predictions of weather, occupancy and real-time electricity prices into the optimization problems to achieve the energy-and-cost saving tasks. For computational efficiency, a simple-structured room thermal model and steady-state performance maps of inverter ACs are developed and integrated. Two types of MPC controllers with different level of complexities are designed for comparison. Simulation results show that the MPC of inverter ACs can effectively improve the thermal comfort at the beginning of occupation, shift the peak power demands, reduce the total energy consumptions and reduce the electricity costs

Effects of price-responsive residential demand on retail and wholesale power market operations

This paper describes a computational platform for studying the effects of price-responsive residential demand for air-conditioning (A/C) on integrated retail and wholesale power market operations. The physical operations of the A/C system are represented by means of the physics-based equivalent thermal parameter model. Residential A/C energy usage levels are determined by means of a stochastic dynamic-programming optimization in which the daily comfort attained by the resident is optimally traded off against his daily energy costs, conditional on retail energy prices, environmental conditions, and A/C operational constraints. An example is provided to illustrate the dynamic feedback loop connecting residential A/C load, the energy prices determined at wholesale conditional on A/C load, and the retail energy prices offered to residential A/C consumers by wholesale energy buyers

Braided Cobwebs: Cautionary Tales for Dynamic Retail Pricing in End-to-End Power Systems

This study investigates the effects of dynamic-price retail contracting on end-to-end power system operations. Performance is evaluated by means of carefully defined metrics for system stability, market efficiency, and market participant welfare. The study is carried out for an Integrated Retail and Wholesale (IRW) Test Case for which households have smart (price-responsive) air-conditioning (A/C) systems. A simplified version of the IRW Test Case with a directly postulated linear household demand curve is first used to derive, analytically, a set of necessary and sufficient conditions for system stability under dynamic-price retail contracting. A key finding is that dynamicprice retail contracts induce braided cobweb dynamics consisting of two interwoven cycles for power and price outcomes that can exhibit point convergence, limit-cycle convergence, or divergence depending on a small set of structural parameters. Outcomes are then reported for a dynamic welfare sensitivity study undertaken using the full IRW Test Case with smart household A/C systems. One surprising finding is that dynamic-price retail contracts with a positive price mark-up result in worse welfare outcomes for generators and household residents than flat-rate retail contracts for treatments exhibiting convergent cobweb dynamics

A Transactive Energy Approach to Distribution System Design: Household Formulation

A household model is formulated to facilitate careful development and performance testing of bid-based transactive energy system (TES) designs with voluntary customer participation. The optimal general bid-function form for households with thermostatically controlled loads is derived from dynamic programming principles, based solely on general household thermal dynamic and welfare attributes. Quantitative forms are determined for these optimal bid functions, given quantitative forms for these attributes. These quantitative attributes are used to construct representative household types based on clusterings of correlated parameter values. Bid comparison, peak-load reduction, and load-matching test cases conducted for a 123-bus distribution system operating under a generic bid-based TES design illustrate the usefulness of our methods for ensuring TES designs are aligned with local customer goals and constraints

Braided Cobwebs: Cautionary Tales for Dynamic Pricing in Retail Electric Power Markets

This study investigates the effects of dynamic-price retail contracting on integrated retail and wholesale (IRW) power market operations. Performance is evaluated by means of carefully defined metrics for system stability and market participant welfare. The study is carried out for an IRW Test Case for which 500 households have price-responsive air-conditioning systems. It is shown that dynamic-price retail contracting can give rise to braided cobweb dynamics consisting of two interwoven cycles for power and price levels exhibiting either stability or instability depending on system conditions. Moreover, even in stable cases, dynamic-price retail contracts generally result in worse welfare outcomes for households than flat-rate retail contracts

Intelligent Residential Air-Conditioning System With Smart-Grid Functionality

This paper sets forth a novel intelligent residential air-conditioning (A/C) system controller that has smart grid functionality. The qualifier “intelligent” means the A/C system has advanced computational capabilities and uses an array of environmental and occupancy parameters in order to provide optimal intertemporal comfort/cost trade-offs for the resident, conditional on anticipated retail energy prices. The term “smart-grid functionality” means that retail energy prices can depend on wholesale energy prices. Simulation studies are used to demonstrate the capabilities of the proposed A/C system controller.This is an accepted manuscript of an article from IEEE Transactions on Smart Grid 3 (2012): 2240, doi:10.1109/TSG.2012.2215060. Posted with permission</p

Optimal Demand Response Strategy in Electricity Markets through Bi-level Stochastic Short-Term Scheduling

Current technology in the smart monitoring including Internet of Things (IoT) enables the electricity network at both transmission and distribution levels to apply demand response (DR) programs in order to ensure the secure and economic operation of power systems. Liberalization and restructuring in the power systems industry also empowers demand-side management in an optimum way. The impacts of DR scheduling on the electricity market can be revealed through the concept of DR aggregators (DRAs), being the interface between supply side and demand side. Various markets such as day-ahead and real-time markets are studied for supply-side management and demand-side management from the Independent System Operator (ISO) viewpoint or Distribution System Operator (DSO) viewpoint. To achieve the research goals, single or bi-level optimization models can be developed. The behavior of weather-dependent renewable energy sources, such as wind and photovoltaic power generation as uncertainty sources, is modeled by the Monte-Carlo Simulation method to cope with their negative impact on the scheduling process. Moreover, two-stage stochastic programming is applied in order to minimize the operation cost. The results of this study demonstrate the importance of considering all effective players in the market, such as DRAs and customers, on the operation cost. Moreover, modeling the uncertainty helps network operators to reduce the expenses, enabling a resilient and reliable network.A tecnologia atual na monitorização inteligente, incluindo a Internet of Things (IoT), permite que a rede elétrica ao nível da transporte e distribuição faça uso de programas de demand response (DR) para garantir a operação segura e económica dos sistemas de energia. A liberalização e a reestruturação da indústria dos sistemas de energia elétrica também promovem a gestão do lado da procura de forma otimizada. Os impactes da implementação de DR no mercado elétrico podem ser expressos pelo conceito de agregadores de DR (DRAs), sendo a interface entre o lado da oferta e o lado da procura de energia elétrica. Vários mercados, como os mercados diário e em tempo real, são estudados visando a gestão otimizada do ponto de vista do Independent System Operator (ISO) ou do Distribution System Operator (DSO). Para atingir os objetivos propostos, modelos de otimização em um ou dois níveis podem ser desenvolvidos. O comportamento das fontes de energia renováveis dependentes do clima, como a produção de energia eólica e fotovoltaica que acarretam incerteza, é modelado pelo método de simulação de Monte Carlo. Ainda, two-stage stochastic programming é aplicada para minimizar o custo de operação. Os resultados deste estudo demonstram a importância de considerar todos os participantes efetivos no mercado, como DRAs e clientes finais, no custo de operação. Ainda, considerando a incerteza no modelo beneficia os operadores da rede na redução de custos, capacitando a resiliência e fiabilidade da rede

A feasibility study for the development of sustainable theoretical framework for smart air-conditioning

Air-conditioning as a technical solution to protect inhabitants from excessive heat exposure creates the challenge of expanding global warming and climate change. While air-conditioning has mostly been applied as an improvement to living conditions, health and environmental problems associated with its use frequently occur. Therefore, this study challenges and extends existing knowledge on sustainability-related to smart air-conditioning systems, where social, environmental and economic dynamics were considered. For instance, when exploring renewable-based options, advanced smart control techniques and profitability measures of air-conditioning reinforce the three pillars of sustainability. In addition to eradicating indoor health effects, this also helps to combat climate change through the system’s sustainability. As an exercise in conceptual modelling, the principal component analysis accounts for sustainable planning and its integration into the theoretical framework. The newly proposed photovoltaic solar air-conditioning was optimised using Polysun to demonstrate the significant application of solar energy in air-conditioning systems, thereby reducing the level of energy consumption and carbon emissions. The newly proposed fuzzy proportional-integral-derivative controller and backpropagation neural network were optimised using Matlab to control the indoor temperature and CO2 level appropriately. The controller of the indoor environment was designed, and the proportional-integral-derivative control was utilised as a result of its suitability. The smart controllers were designed to regulate the parameters automatically to ensure an optimised control output. The performance of photovoltaic solar air-conditioning in different temperate climates of Rome, Toulouse and London districts achieved a higher coefficient of performance of 3.37, 3.69 and 3.97, respectively. The system saved significant amount of energy and carbon emissions. The indoor temperature and indoor CO2 possess an appropriate time constant and settling time, respectively. The profitability assessment of the system revealed its adequate efficiency with an overall payback period of 5.5 years