Demand response from thermostatically controlled loads: modelling, control and system-level value

The research area of this thesis concerns the efficient and secure operation of the future low-carbon power system, where alternative sources of control and flexibility will progressively replace the traditional providers of ancillary services i.e. conventional generators. Various options are engaged in this challenge and suit the innovative concept of Smart Grid. Specifically, this thesis investigates the potential of demand side response support by means of thermostatically controlled loads (TCLs). This thesis aims to quantify the impact that a population of thermostatically controlled loads has on the commitment and dispatch of a future power system characterized by a large penetration of renewable energy sources (e.g. wind) that are variable and intermittent. Thanks to their relative insensitivity to temperature fluctuations, thermostatic loads would be able to provide frequency response services and other forms of system services, such as energy arbitrage and congestion relief. These actions in turn enhance the power system operation and support the strict compliance with system security standards. However, the achievement of this transition requires addressing two challenges. The first deals with the design of accurate device models. Significant differences affect the devices’ design included in the same class, leading to different system-level performances. In addition, the flexibility associated to TCLs would be handled more easily by means of models that describes the TCLs dynamics directly as a cluster rather than considering the appliances individually. Second, it is not straightforward achieving satisfactory controllability of a cluster of TCLs for the considered applications. The complexity lies in the typical operation of these devices that has only two power states (on and off) whereas the desired response is continuous. Moreover the control strategy has always to comply with strict device-level temperature constraints as the provision of ancillary services cannot affect the quality of the service of the primary function of TCLs. This thesis addresses the challenges exhibited. Detailed thermal dynamic models are derived for eight classes of domestic and commercial refrigeration units. In addition, a heterogeneous population of TCLs is modelled as a leaky storage unit; this unit describes the aggregate flexibility of a large population of TCLs as a single storage unit incorporating the devices’ physical thermal models and their operational temperature limits. The control problem is solved by means of an initial hybrid controller for frequency response purposes that is afterwards replaced by an advanced controller for various applications. Provided these two elements, a novel demand side response model is designed considering the simultaneous provision of a number of system services and taking into account the effect of the load energy recovery. The model, included in a stochastic scheduling routine, quantifies the system-level operational cost and wind curtailment savings enabled by the TCLs support.Open Acces

Study and analysis of the use of flexibility in local electricity markets

In this work an introduction to Local Electricity Markets (LEM) was done and afterwards evolutionary algorithms (EAs) such as Differential Evolution (DE), HybridAdaptive Differential Evolution (HyDE), Hybrid-Adaptive Differential Evolution with Decay Function (HyDE-DF) and Vortex Search (VS) were applied to a market model in order to test its efficiency and scalability. Then, the market model was expanded adding a network model from the BISITE laboratory and again tests using the evolutionary algorithms were performed. In more detail, first a literature review is done about distributed generation, load flexibility, LEM and EAs. Then a cost optimization problem in Local Electricity Markets is analyzed considering fixed-term flexibility contracts between the distribution system operator (DSO) and aggregators. In this market structure, the DSO procures flexibility while aggregators of different types (e.g., conventional demand response or thermo-load aggregators) offer the service. Its then solved the proposed model using evolutionary algorithms based on the well-known differential evolution (DE). First, a parameter-tuning analysis is done to assess the impact of the DE parameters on the quality of solutions to the problem. Later, after finding the best set of parameters for the “tuned” DE strategies, we compare their performance with other self-adaptive parameter algorithms, namely the HyDE, HyDE-DF, and VS. Overall, the algorithms are able to find near-optimal solutions to the problem and can be considered an alternative solver for more complex instances of the model. After this a network model, from BISITE laboratory, is added to the problem and new analyses are performed using evolutionary algorithms along with MATPOWER power flow algorithms. Results show that evolutionary algorithms support from simple to complex problems, that is, it is a scalable algorithm, and with these results it is possible to perform analyses of the proposed market model.Neste trabalho foi feita uma introdução aos Mercados Locais de Eletricidade (MLE) e posteriormente foram aplicados algoritmos evolutivos (AEs) como Differential Evolution (DE), Hybrid-Adaptive Differential Evolution (HyDE), Hybrid-Adaptive Differential Evolution with Decay Function (HyDE-DF) e Vortex Search (VS) a um modelo de mercado a fim de testar a sua eficiência e escalabilidade. O modelo de mercado foi expandido adicionando uma rede do laboratório BISITE e novamente foram realizados testes usando os algoritmos evolutivos. Em mais detalhe, no trabalho primeiro foi feita uma revisão bibliográfica sobre geração distribuída, flexibilidade de carga, MLE e AEs. É analisado um problema de optimização de custos nos MLE, considerando contratos de flexibilidade a prazo fixo entre os agentes. O distribuidor adquire flexibilidade enquanto que os agregadores de diferentes tipos (por exemplo, os agregadores convencionais de resposta à procura ou de carga térmica) oferecem o serviço. Resolve-se depois o modelo proposto utilizando AEs baseados na conhecida DE. É feita uma análise de afinação de parâmetros para avaliar o impacto dos parâmetros DE na qualidade das soluções para o problema. Após encontrarmos o melhor conjunto de parâmetros para as estratégias DE "afinadas", comparamos o seu desempenho com outros algoritmos de parâmetros autoadaptáveis, nomeadamente o HyDE, HyDE-DF, e VS. Globalmente, os algoritmos são capazes de encontrar soluções quase óptimas para o problema e podem ser considerados um solucionador alternativo para instâncias mais complexas do modelo. Então um modelo de rede, do laboratório BISITE, é acrescentado ao problema e novas análises são realizadas utilizando algoritmos evolutivos juntamente com algoritmos de fluxo de potência MATPOWER. Os resultados mostram que os algoritmos evolutivos suportam desde problemas simples a complexos, ou seja, é um algoritmo escalável, e com estes resultados é possível realizar análises do modelo de mercado proposto

Optimal dispatch of uncertain energy resources

The future of the electric grid requires advanced control technologies to reliably integrate high level of renewable generation and residential and small commercial distributed energy resources (DERs). Flexible loads are known as a vital component of future power systems with the potential to boost the overall system efficiency. Recent work has expanded the role of flexible and controllable energy resources, such as energy storage and dispatchable demand, to regulate power imbalances and stabilize grid frequency. This leads to the DER aggregators to develop concepts such as the virtual energy storage system (VESS). VESSs aggregate the flexible loads and energy resources and dispatch them akin to a grid-scale battery to provide flexibility to the system operator. Since the level of flexibility from aggregated DERs is uncertain and time varying, the VESSs’ dispatch can be challenging. To optimally dispatch uncertain, energy-constrained reserves, model predictive control offers a viable tool to develop an appropriate trade-off between closed-loop performance and robustness of the dispatch. To improve the system operation, flexible VESSs can be formulated probabilistically and can be realized with chance-constrained model predictive control. The large-scale deployment of flexible loads needs to carefully consider the existing regulation schemes in power systems, i.e., generator droop control. In this work first, we investigate the complex nature of system-wide frequency stability from time-delays in actuation of dispatchable loads. Then, we studied the robustness and performance trade-offs in receding horizon control with uncertain energy resources. The uncertainty studied herein is associated with estimating the capacity of and the estimated state of charge from an aggregation of DERs. The concept of uncertain flexible resources in markets leads to maximizing capacity bids or control authority which leads to dynamic capacity saturation (DCS) of flexible resources. We show there exists a sensitive trade-off between robustness of the optimized dispatch and closed-loop system performance and sacrificing some robustness in the dispatch of the uncertain energy capacity can significantly improve system performance. We proposed and formulated a risk-based chance constrained MPC (RB-CC-MPC) to co-optimize the operational risk of prematurely saturating the virtual energy storage system against deviating generators from their scheduled set-point. On a fast minutely timescale, the RB-CC-MPC coordinates energy-constrained virtual resources to minimize unscheduled participation of ramp-rate limited generators for balancing variability from renewable generation, while taking into account grid conditions. We show under the proposed method it is possible to improve the performance of the controller over conventional distributionally robust methods by more than 20%. Moreover, a hardware-in-the-loop (HIL) simulation of a cyber-physical system consisting of packetized energy management (PEM) enabled DERs, flexible VESSs and transmission grid is developed in this work. A predictive, energy-constrained dispatch of aggregated PEM-enabled DERs is formulated, implemented, and validated on the HIL cyber-physical platform. The experimental results demonstrate that the existing control schemes, such as AGC, dispatch VESSs without regard to their energy state, which leads to unexpected capacity saturation. By accounting for the energy states of VESSs, model-predictive control (MPC) can optimally dispatch conventional generators and VESSs to overcome disturbances while avoiding undesired capacity saturation. The results show the improvement in dynamics by using MPC over conventional AGC and droop for a system with energy-constrained resources