Stochastic dynamic optimization of consumption and the induced price elasticity of demand in smart grids

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 75-77).This thesis presents a mathematical model of consumer behavior in response to stochastically-varying electricity prices, and a characterization of price-elasticity of demand created by optimal utilization of storage and the flexibility to shift certain demands to periods of lower prices. The approach is based on analytical characterization of the consumer's optimal policy and the associated value function in a finite-horizon stochastic dynamic programming framework. A general model is first presented, which incorporates both load-shifting and storage, and then, the model is decoupled into two subproblems, one for load-shifting and the other for storage. The study of optimal utilization of storage, which is performed analytically and in the presence of ramp constraints, reveals, as a particularly compelling finding, that the value function is a convex piece-wise linear function of the storage state. Moreover, it is shown that the expected monetary value of storage increases with price volatility, and that when the ramping rate is finite, the value of storage saturates quickly as the capacity increases, regardless of price volatility. Furthermore, it is shown that although the demand for electricity is often deemed to be highly inelastic, optimal utilization of local storage capacity induces a considerable amount of price elasticity of demand. The study of the load-shifting problem is performed under both perfect and partial information about price distribution. It is shown that load-shifting induces considerable consumer savings that increase with price volatility. Furthermore, it is shown that the opportunity to optimally schedule the shiftable loads creates a considerable amount of price elasticity, even when the aggregate consumption over a long period remains insensitive to price variations.by Ali Faghih.S.M

Flexibility market for congestion management in smart grids

Mención Internacional en el título de doctorCurrent power systems are facing several sustainability challenges to meet the increasing demand of electricity. In addition, there is a global direction to increase the share of renewable energy sources in the power generation mix and energy efficiency. In the face of all such challenges, smart grids were incepted. Smart grids are modernized power systems that integrate state-of-the art communication and information technology to facilitate the bidirectional flow of information and electricity between the supply and demand sides. The resilience of smart grids can pave the way for having more flexibility at the distribution level of the power systems. Demand response (DR) programs are considered one of the sources of system flexibility and it is one of the main components of smart grids. DR can be defined as the willingness of customers to alter their electricity consumption profile in response to price signals. Transmission system operators have been implementing demand response programs in a straightforward fashion for several years now. For example, by having energy prices that are expensive during on-peak periods and low-priced at off-peak periods. Other type of DR programs introduces price signals when grid reliability is compromised and a reduction in energy consumption is necessary. In this way, customers can plan their activities accordingly in order to save money. Now, a new era of technology, artificial intelligence and the so-called “internet of things”, have provided new ways to explore the full potential of demand response, by allowing to alter loads in a much more dynamic and precise manner, thus optimizing the operation of grid assets. This thesis focuses on one of the main types of DR programs which is demand flexibility. Demand flexibility is the ability of the demand-side customers to adjust their load profiles in response to an external market signal. On the short- and medium-term periods, distribution system operators (DSOs) can take advantage of the flexibility of demand to mitigate network congestions caused by increased peaks or high penetration of renewable energy. On the long-term period, DSOs can include demand flexibility in their network expansion planning process for future demand growth. The optimal usage of demand flexibility can help in postponing needed investments for upgrading the networks’ capacity. Demand flexibility can be acquired through market-based solutions which can deliver cost-efficient flexibility services for several market agents by facilitating competition between different flexibility providers. Market mechanisms are considered by policy makers as the optimal solution for flexibility access. With respect to that, this thesis proposes a comprehensive framework for a distribution-level flexibility market, called “Flex-DLM” that enables and facilitates the trading of demand flexibility between the distribution system operator, as the main buyer, and aggregators, as sellers representing flexible consumers. Two types of demand flexibility services were modelled, which are: 1- Up-regulation flexibility (UREG), which corresponds to load decrease volumes, and 2- Down-regulation flexibility (DREG), which corresponds load increase volumes. In addition, the payback effect, which is a common event to the activation of demand flexibility, is considered for both types of flexibility services. Also, the distribution network constraints were modelled, which represents the power flow constraints of the network, which is key to present a realistic model for the flexibility market. In the Flex-DLM, the DSO is considered as the market operator who is responsible of clearing the market, while making sure the network congestions are mitigated. The Flex-DLM operates on two timeframes which are day-ahead and real-time with an objective to provide the DSO with flexibility products that can help it in the congestion management process. In addition to this, the uncertainty of demand is taken into consideration to prevent the DSO from procuring inaccurate amounts of demand flexibility. A new option is introduced in the day-ahead Flex-DLM, called the right-to-use (RtU) that allows the DSO to reserve the right to activate demand flexibility during the day-ahead period for congestions that have low probability of occurrence on the following operation day. In this way, the DSO can call upon this option in real-time if the congestion takes place. Also, the uncertainty behind the customers’ commitment to the flexibility activation requests and amounts is taken into consideration. In this thesis, the decision-making process of the DSO for optimizing its choice of demand flexibility and minimizing its total cost is modelled. Two methods were carried out for the optimization model proposed in this work. The first method follows a deterministic approach, where the objective is to optimize the DSO’s cost and clear the Flex-DLM during the day-ahead period only, without taking into account the uncertainty of demand and the uncertainty of consumers’ participation. The second method follows probabilistic approach, which considers the demand uncertainty during the day-ahead and real-time periods and models the uncertainty behind the customers’ commitment. Both optimization methods were integrated with an optimal power flow (OPF) solver tool in order to check the technical validity of the activated flexibility services and to make sure that the payback effect does not cause further congestions in the network. The advantage of the proposed framework is that it requires minimum regulatory changes and it does not involve the DSO in any electricity trading. Also, the proposed optimization method can be integrated with any OPF solver tool. Different distribution feeders obtained from a distribution network located in Spain were used to check the validity of the proposed framework and the decision-making process. The case studies are divided into two parts: 1- The first part applies the proposed flexibility framework from a deterministic perspective and 2- The second part applies the Flex-DLM framework considering all uncertainties, which corresponds to the probabilistic optimization approach. Finally, to help the DSO in the long-term planning process of its local network, a cost & benefit analysis is carried out to value the economic impact of implementing demand flexibility programs as an alternate solution to conventional network upgradesLos sistemas de energía actuales se enfrentan a varios desafíos de sostenibilidad para satisfacer la creciente demanda de electricidad. Además, existe una clara tendencia a aumentar la proporción de fuentes renovables de energía en la generación de energía y así como hacia la eficiencia energética. Como parte de la respuesta a estos desafíos, se iniciaron las redes inteligentes. Las redes inteligentes son sistemas de energía modernizados que integran tecnología de comunicación e información de última generación para facilitar el flujo bidireccional de información y electricidad entre la oferta y la demanda. La utilización de las redes inteligentes pretende facilitar el empleo de la flexibilidad en la red de distribución de los sistemas eléctricos. Los programas de gestión de la demanda se consideran una de las fuentes de flexibilidad del sistema y es uno de los puntos sobre los que se apoyan las redes inteligentes. La gestión de la demanda se puede definir como la disposición de los clientes a alterar su perfil de consumo de electricidad en respuesta a las señales de precios. Los operadores de sistemas de transporte han estado implementando programas de respuesta a la demanda de manera directa desde hace varios años. Por ejemplo, la diferencia entre precios altos y bajos en el mercado mayorista introduce un incentivo para el consumo en horas de menor precio. Otro tipo de programas de gestión de la demanda introduce señales de precios cuando la fiabilidad de la red se ve comprometida y es necesaria una reducción en el consumo de energía. De esta manera, los consumidores pueden planificar sus actividades en consecuencia para ahorrar costes. Ahora, una nueva era de la tecnología, la inteligencia artificial y el llamado "internet de las cosas" han proporcionado nuevas formas de explorar el potencial completo de la respuesta de la demanda, al permitir alterar las cargas de una manera mucho más dinámica y precisa, optimizando así la utilización de los activos de red. Esta tesis se centra en uno de los principales tipos de programas de DR que es la flexibilidad de la demanda. La flexibilidad de la demanda es la capacidad de los clientes del lado de la demanda para ajustar sus perfiles de carga en respuesta a una señal del mercado externo. En los períodos a corto y mediano plazo, los operadores de sistemas de distribución pueden aprovechar la flexibilidad de la demanda para mitigar las congestiones en la red causadas por el aumento de los picos de demanda o la alta penetración de energía renovable. En el período a largo plazo, los distribuidores pueden incluir la flexibilidad de la demanda en su proceso de planificación de expansión de la red para el crecimiento futuro de la demanda. El uso óptimo de la flexibilidad de la demanda puede ayudar a posponer las inversiones necesarias para mejorar la capacidad de las redes. La flexibilidad de la demanda se puede conseguir mediante soluciones basadas en el mercado que pueden ofrecer servicios de flexibilidad rentables para varios agentes del mercado al facilitar la competencia entre diferentes proveedores de flexibilidad. Los reguladores suelen considerar que son los mecanismos de mercado los que dan la solución óptima para la gestión de la flexibilidad. En relación con estos temas, esta tesis propone un marco integral para un mercado de flexibilidad a en la red de distribución, denominado “Flex-DLM” que permite y facilita el comercio de flexibilidad de demanda entre el operador del sistema de distribución, como el principal comprador, y los agregadores, como vendedores que representan a los consumidores flexibles. Se han modelado dos tipos de servicios de flexibilidad de demanda, que son: 1- Flexibilidad a subir (UREG), que corresponde a un requerimiento disminución de carga, y 2- Flexibilidad a bajar (DREG), que corresponde a un requerimiento de aumento de carga. Además, el efecto de rebote, o consumo posterior al uso de la flexibilidad, que es un fenómeno común tras la activación de la flexibilidad de la demanda, se tiene en cuenta para ambos tipos de servicios de flexibilidad. Además, se han modelado las restricciones de la red de distribución, que representan las restricciones de flujo de potencia de la red, que es clave para presentar un modelo realista para el mercado de flexibilidad. En el mercado Flex-DLM propuesto, se considera al distribuidor como el operador responsable de despejar el mercado, al tiempo que se encarga de mitigar las congestiones de la red. El Flex-DLM opera en dos marcos de tiempo: el diario y el tiempo real con el objetivo de proporcionar al distribuidor productos flexibles que puedan ayudarlo en el proceso de gestión de la congestión. Además de esto, la incertidumbre de la demanda se tiene en cuenta para evitar que el distribuidor adquiera cantidades incorrectas de flexibilidad de la demanda. Se introduce una nueva opción en el Flex-DLM del día siguiente, denominado derecho de uso que le permite al distribuidor reservar el derecho de activar la flexibilidad de la demanda durante el período del día anterior para congestiones que tienen poca probabilidad de ocurrencia en el siguiente día de operación. De esta manera, el distribuidor puede recurrir a esta opción en tiempo real si se produce la congestión. Además, se tiene en cuenta la incertidumbre sobre del compromiso de cumplimiento de los clientes con los requerimientos y las cantidades de energía activadas durante el proceso de gestión de la flexibilidad. En esta tesis, se modela asimismo el proceso de toma de decisiones del DSO para optimizar su elección de flexibilidad de demanda y minimizar su costo total. Se llevaron a cabo dos métodos para el modelo de optimización propuesto en este trabajo. El primer método sigue un enfoque determinista, donde el objetivo es optimizar el coste de la flexibilidad para el distribuidor y eliminar el Flex-DLM solo durante el mercado diario , sin tener en cuenta la incertidumbre de la demanda y la de la participación de los consumidores. El segundo método sigue un enfoque probabilístico, que considera la incertidumbre de la demanda durante los períodos diarios y en tiempo real y modela la incertidumbre del compromiso de los clientes. Ambos métodos de optimización se integraron con una herramienta de solución de flujo de potencia óptimo (OPF) para verificar la validez técnica de los servicios de flexibilidad activados y asegurar que el efecto de recuperación no cause más congestiones en la red. La ventaja del marco propuesto es que requiere cambios regulatorios mínimos y no involucra al DSO en ningún comercio de electricidad. Además, el método de optimización propuesto se puede integrar con cualquier herramienta de solución OPF. Se han utiliado diferentes líneas de distribución obtenidos de una red de distribución ubicada en España para verificar la validez del marco propuesto y el proceso de toma de decisiones. Los estudios de caso se dividen en dos partes: 1- La primera parte aplica el marco de flexibilidad propuesto desde una perspectiva determinista y 2- La segunda parte aplica el marco Flex-DLM considerando todas las incertidumbres, que corresponden al enfoque de optimización probabilística. Finalmente, para ayudar al distribuidor en el proceso de planificación a largo plazo de su red local, se lleva a cabo un análisis coste - beneficio para valorar el impacto económico de la implementación de programas de flexibilidad de la demanda como una solución alternativa a las actualizaciones de red convencionales.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Hortensia Elena Amaris Duarte.- Secretario: Milan Prodanovic.- Vocal: Barry Patrick Haye

Modeling and Communicating Flexibility in Smart Grids Using Artificial Neural Networks as Surrogate Models

Increasing shares of renewable energies and the transition towards electric vehicles pose major challenges to the energy system. In order to tackle these in an economically sensible way, the flexibility of distributed energy resources (DERs), such as battery energy storage systems, combined heat and power plants, and heat pumps, needs to be exploited. Modeling and communicating this flexibility is a fundamental step when trying to achieve control over DERs. The literature proposes and makes use of many different approaches, not only for the exploitation itself, but also in terms of models. In the first step, this thesis presents an extensive literature review and a general framework for classifying exploitation approaches and the communicated models. Often, the employed models only apply to specific types of DERs, or the models are so abstract that they neglect constraints and only roughly outline the true flexibility. Surrogate models, which are learned from data, can pose as generic DER models and may potentially be trained in a fully automated process. In this thesis, the idea of encoding the flexibility of DERs into ANNs is systematically investigated. Based on the presented framework, a set of ANN-based surrogate modeling approaches is derived and outlined, of which some are only applicable for specific use cases. In order to establish a baseline for the approximation quality, one of the most versatile identified approaches is evaluated in order to assess how well a set of reference models is approximated. If this versatile model is able to capture the flexibility well, a more specific model can be expected to do so even better. The results show that simple DERs are very closely approximated, and for more complex DERs and combinations of multiple DERs, a high approximation quality can be achieved by introducing buffers. Additionally, the investigated approach has been tested in scheduling tasks for multiple different DERs, showing that it is indeed possible to use ANN-based surrogates for the flexibility of DERs to derive load schedules. Finally, the computational complexity of utilizing the different approaches for controlling DERs is compared

Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively