Optimal control for efficient electric heating

The purpose of this study is to investigate methods of reducing the cost of electricity consumption. Utility companies must forecast and adjust for power demand. Utilities desire a 1:1 load factor ratio between peak energy usage and average usage. During peak hours, electricity production is most expensive. There are two major methods for reducing the peak power for Thermostatically Controlled Loads (TCL), such as electric water heaters, air conditioners, or heat pumps: a) Classic Demand Side Management (DSM) methods such as demand shifting and electricity pricing tariffs, and b) Advanced DSM load control methods. This thesis will focus on analyzing the advanced control methods to reduce peak power and to save energy. The use of space heating and TCL loads for reducing electricity consumption and peak demand production is an important research area, considering that the energy consumption of most of US single-family residential homes is from controllable appliances. An experimental thermal identification system utilizing first and second order mathematical models has been developed at WCU.Using these models, a new proportional (P-Only) and proportional integral (PI) controller are investigated and assessed for improvements of reduction of peak power and energy savings for a TCL compared to the traditional Bang-Bang Controller in a resistive space heating prototype. Comparative results between simulation and experimental work validated the linearity of power electronics controller. Linearization was achieved by identifying a mathematical relationship that eliminates quadratic power function as well as Buck converter’s nonlinearity. Temperature disparity and input power characteristics were improved using this new converter for controlling the space heater. The system developed is an important step toward energy savings, temperature improvements and demand side management for reducing peak demand

Simulation modeling for energy consumption of residential consumers in response to demand side management.

Energy efficiency in the electricity distribution system continues to gain importance as demand for electricity keeps rising and resources keep diminishing. Achieving higher energy efficiency by implementing control strategies and demand response (DR) programs has always been a topic of interest in the electric utility industry. The advent of smart grids with enhanced data communication capabilities propels DR to be an essential part of the next generation power distribution system. Fundamentally, DR has the ability to charge a customer the true price of electricity at the time of use, and the general perception is that consumers would shift their load to a cheaper off-peak period. Consequently, when designing incentives most DR literature assumes consumers always minimize total electricity cost when facing energy consumption decisions. However, in practice, it has been shown that customers often override financial incentives if they feel strongly about the inconvenience of load-shifting arrangements. In this dissertation, an energy consumption model based on consumers‟ response to both cost and convenience/comfort is proposed in studying the effects of differential pricing mechanisms. We use multi-attribute utility functions and a model predictive control mechanism to simulate consumer behavior of using non-thermostatic loads vi (prototypical home appliances) and thermostatically controlled load (HVAC). The distributed behavior patterns caused by risk nature, thermal preferences, household size, etc. are all incorporated using an object-oriented simulation model to represent a typical residential population. The simulation based optimization platform thus developed is used to study various types of pricing mechanisms including static and dynamic variable pricing. There are many electric utilities that have applied differential pricing structures to influence consumer behavior. However, majority of current DR practices include static variable pricings, since consumer response to dynamic prices is very difficult to predict. We also study a novel pricing method using demand charge on coincident load. Such a pricing model is based on consumers‟ individual contribution to the monthly system peak, which is highly stochastic. We propose to use the conditional Markov chain to calculate the probability that the system will reach a peak, and subsequently simulate consumers‟ behavior in response to that peak. Sensitivity analysis and comparisons of various rate structures are done using simulation. Overall, this dissertation provides a simulation model to study electricity consumers‟ response to DR programs and various rate structures, and thus can be used to guide the design of optimal pricing mechanism in demand side management

The State of the Art in Model Predictive Control Application for Demand Response

Demand response programs have been used to optimize the participation of the demand side. Utilizing the demand response programs maximizes social welfare and reduces energy usage. Model Predictive Control is a suitable control strategy that manages the energy network, and it shows superiority over other predictive controllers. The goal of implementing this controller on the demand side is to minimize energy consumption, carbon footprint, and energy cost and maximize thermal comfort and social welfare.  This review paper aims to highlight this control strategy\u27s excellence in handling the demand response optimization problem. The optimization methods of the controller are compared. Summarization of techniques used in recent publications to solve the Model Predictive Control optimization problem is presented, including demand response programs, renewable energy resources, and thermal comfort. This paper sheds light on the current research challenges and future research directions for applying model-based control techniques to the demand response optimization problem

Improving power quality efficient in demand response: Aggregated heating, ventilation and air-conditioning systems

This study aims to identify the role of aggregated heating, ventilation, and air conditioning (HVAC) loads based on system characteristics using the lazy state switching control mode focusing on the overall power consumption rather individual response speed. This study is attempted to provide secondary frequency regulation using aggregated HVAC loads with more stable operation with the lazy state switching control mode based on conditional switching of the HVAC unit’s working state. The stability of power consumption improves power quality in smart grid design and operation. The aggregated HVAC must reach a stable condition before tracking the automatic generation control signal and fully developed smart grids complex structure. Still, HVAC slowed responses make inappropriate for faster demand response services. Unsuitable control algorithm leads to system instability and HVAC unit overuse. An extended command processing on the client side is proposed to deal with the adjusting command. The unique advantages of the proposed algorithm are three folds. (1) the control algorithm preserves its working state and has nothing conflicting with the lockout constraints for individual system units; (2) the control algorithm shows promising performance in smoothing the overall power consumption for the aggregated population; and (3) the control logic is fully compatible with other control algorithms. The proposed modeling and control strategy are validated against simulations of thousands of units, and the simulation result indicates that the proposed approach has promising performance in smoothing the power consumption of aggregate units’ population

Participation of distributed loads in power markets that co-optimize energy and reserves

Thesis (Ph.D.)--Boston UniversityAs the integration of Renewable Generation into today's Power Systems is progressing rapidly, capacity reserve requirements needed to compensate for the intermittency of renewable generation is increasing equally rapidly. A major objective of this thesis is to promote the affordability of incremental reserves by enabling loads to provide them through demand response. Regulation Service (RS) reserves, a critical type of bi-directional Capacity Reserves, are provided today by expensive and environmentally unfriendly centralized fossil fuel generators. In contrast, we investigate the provision of low-cost RS reserves by the demand-side. This is a challenging undertaking since loads must first promise reserves in the Hour Ahead Markets, and then be capable of responding to the dynamic ISO signals by adjusting their consumption effectively and efficiently. To this end, we use Stochastic Control, Optimization Theory, and Approximate Dynamic Programming to develop a decision support framework that assists Smart Neighborhood Operators or Smart Building Operators (SNOs/SBOs) to become demand-side-providers of RS reserve. We first address the SNO/SBO short time scale operational task of responding to the Independent System Operator's (ISO) dynamic RS requests. We start by developing a model-based Markovian decision problem that trades off ISO RS tracking against demand response related utility loss. Starting with a model based approach we obtain near optimal operational policies through a novel approximate policy iteration technique and an actor critic approach which is robust to partial knowledge of the underlying system dynamics. We then abandon the model based terrain and solve the dynamic operational problem through reinforcement learning that is capable of modeling a population of duty cycle appliances with realistic thermodynamics. We finally propose a smart thermostat design and develop an adaptive control policy that can drive the smart thermostat effectively. The latter approach is particularly suited for systems whose dynamics and dynamically changing consumer preferences are not known or observed beyond the total power consumption. We then address the SNO/SBO task of bidding RS reserves to the hour ahead market. This task determines the maximal RS reserves that the SNO/SBO can promise based on information available at the beginning of an hour, so as to maximize the associated hour-ahead revenues minus the expected average operating cost that will be incurred during the operational task to follow. To accomplish this task, we (i) develop probabilistic constraints that model the feasible maximum reserves which can be offered to the market without exceeding the SNO/SBO's ability to later track the unanticipated dynamic ISO RS signal, and (ii) calibrate a describing function that approximates the average operational cost as a function of the maximal reserves that can be feasibly offered in the day ahead market. The above is made possible by statistical analysis of the controlled system's stochastic dynamics and properties of the optimal dynamic policies that we derive. The contribution of the thesis is twofold: The solution of a difficult stochastic control problem that is crucial for effective demand-response-based provision of regulation service, and, the characterization of key properties of the stochastic control problem solution, which allow its integration into the hour-ahead market bidding problem

Performance of Smart Homes for participating in Electricity Markets

Devido ao crescente consumo de energia proveniente de residências, o comportamento dos consumidores de Smart Homes vem sendo estudado nos últimos anos, com o objetivo de otimizar a eficiência energética e o consumo de energia. Além disso, é necessário otimizar o consumo de energia da casa para minimizar custos e reduzir as emissões de gases. Atualmente, o Mercado de Energia Elétrica tem se mostrado muito mais competitivo devido ao surgimento de fontes renováveis ​​de energia e à participação ativa do consumidor no mercado, utilizando programas de demand response. O objetivo deste projeto é desenvolver e melhorar um código-fonte para permitir a gestão da demanda de uma casa inteligente, incluindo geração de energia renovável, veículo elétrico e outros aparelhos inteligentes e dispositivos/electrodomésticos elétricos. Além disso, a redução do custo esperado do consumo de energia e o aumento do conforto do consumidor são considerados como metas do projeto.Due to the rising energy consumption of residential consumers, smart home consumers' behaviour is being studied in the last years to achieve optimal energy efficiency and power consumption. Also, there is a need to optimize house energy consumption to minimize costs and reduce gas emissions. Nowadays, Electricity Market has been much more competitive due to the rising of renewable energy sources and consumer's active participation in the market, using demand response programs. This project aims to develop and improve a source code to allow demand management of a Smart Home, including renewable energy generation, electric vehicle and other smart appliances and electrical devices. Furthermore, the reduction of the expected cost of energy consumption and the rise of consumer's comfort are considered as goals for the project

Achieving energy efficient districts: contributions through large-scale characterization and demand side management.

Buildings are increasingly expected to be more efficient and sustainable since they are essential to energy policies and climate change mitigation efforts. For this reason, it is very important to develop new energy models, with special attention to the residential sector. The present Thesis aims to justify the selection of the district scale as the optimal one to improve the energy performance of the built environment. In this way, renewable energy integration may be increased and innovative approaches such as demand side management may be carried out through the accurate characterization of districts. Several applications are shown to evaluate the solar potentials and the energy demands for entire regions by using 3D city models. The advantages offered by demand side management approaches in buildings and districts are investigated, presenting two applications that benefit from dynamic pricing strategies or the participation in reserve markets. The drawbacks of most current approaches on a large scale are highlighted, and a new tool capable of performing dynamic simulations of whole districts in a user-friendly and accurate way is presented. In addition, a methodology for a proper characterization of districts through monitoring is developed, validated, and used for two applications. The first one characterizes a district consisting of buildings with a limited use of air-conditioning, and the second one evaluates the benefits that could be obtained from the exploitation of the synergies between the buildings of a district. As a last contribution of this Thesis, a new comprehensive methodology for the characterization and optimization of any existing district is proposed.Se espera que los edificios sean cada vez más eficientes y sostenibles, puesto que son esenciales para las políticas energéticas y los esfuerzos hacia la mitigación del cambio climático. Por esta razón, es muy importante desarrollar nuevos modelos energéticos, con especial atención al sector residencial. La presente Tesis parte de que la escala de distrito es la óptima para mejorar el comportamiento de la edificación. Además, permite aumentar la integración de energías renovables y llevar a cabo planteamientos innovadores como la gestión de la demanda a través de una precisa caracterización de los distritos. Se muestran varias aplicaciones para la evaluación de los potenciales solares y las demandas energéticas de regiones enteras, usando modelos 3D de ciudades. Las ventajas ofrecidas por los procedimientos de gestión de la demanda en edificios y distritos también son investigadas, presentando dos aplicaciones que se benefician de estrategias de tarificación dinámica o de la participación en los mercados de reserva. Las desventajas de la mayoría de procedimientos actuales a gran escala también son destacadas, y se presenta una nueva herramienta capaz de llevar a cabo simulaciones dinámicas de distritos completos de forma simple y precisa. Además, se desarrolla una metodología para la caracterización apropiada de distritos a través de monitorización, validada y empleada en dos aplicaciones. La primera trata la caracterización un distrito compuesto por edificios con un uso limitado de la climatización, y la segunda la evaluación de los beneficios que podrían obtenerse de la explotación de las sinergias entre los edificios de un distrito. Como última contribución de la Tesis, se propone una nueva metodología completa para la caracterización y optimización de cualquier distrito existente.Premio Extraordinario de Doctorado U

Scaling energy management in buildings with artificial intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen