Improved Battery Models of an Aggregation of Thermostatically Controlled Loads for Frequency Regulation

Recently it has been shown that an aggregation of Thermostatically Controlled Loads (TCLs) can be utilized to provide fast regulating reserve service for power grids and the behavior of the aggregation can be captured by a stochastic battery with dissipation. In this paper, we address two practical issues associated with the proposed battery model. First, we address clustering of a heterogeneous collection and show that by finding the optimal dissipation parameter for a given collection, one can divide these units into few clusters and improve the overall battery model. Second, we analytically characterize the impact of imposing a no-short-cycling requirement on TCLs as constraints on the ramping rate of the regulation signal. We support our theorems by providing simulation results.Comment: to appear in the 2014 American Control Conference - AC

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

Value of thermostatic loads in future low-carbon Great Britain system

This paper quantifies the value of a large population of heterogeneous thermostatically controlled loads (TCLs). The TCL dynamics are regulated by means of an advanced demand side response model (DSRM). It optimally determines the flexible energy/power consumption and simultaneously allocates multiple ancillary services. This model explicitly incorporates the control of dynamics of the TCL recovery pattern after the provision of the selected services. The proposed framework is integrated in a mixed integer linear programming formulation for a multi-stage stochastic unit commitment. The scheduling routine considers inertia-dependent frequency response requirements to deal with the drastic reduction of system inertia under future low-carbon scenarios. Case studies focus on the system operation cost and CO2 emissions reductions for individual TCLs for a) different future network scenarios, b) different frequency requirements, c) changes of TCL parameters (e.g. coefficient of performance, thermal insulation etc.)

Wind Farms and Flexible Loads Contribution in Automatic Generation Control: An Extensive Review and Simulation

With the increasing integration of wind energy sources into conventional power systems, the demand for reserve power has risen due to associated forecasting errors. Consequently, developing innovative operating strategies for automatic generation control (AGC) has become crucial. These strategies ensure a real-time balance between load and generation while minimizing the reliance on operating reserves from conventional power plant units. Wind farms exhibit a strong interest in participating in AGC operations, especially when AGC is organized into different regulation areas encompassing various generation units. Further, the integration of flexible loads, such as electric vehicles and thermostatically controlled loads, is considered indispensable in modern power systems, which can have the capability to offer ancillary services to the grid through the AGC systems. This study initially presents the fundamental concepts of wind power plants and flexible load units, highlighting their significant contribution to load frequency control (LFC) as an important aspect of AGC. Subsequently, a real-time dynamic dispatch strategy for the AGC model is proposed, integrating reserve power from wind farms and flexible load units. For simulations, a future Pakistan power system model is developed using Dig SILENT Power Factory software (2020 SP3), and the obtained results are presented. The results demonstrate that wind farms and flexible loads can effectively contribute to power-balancing operations. However, given its cost-effectiveness, wind power should be operated at maximum capacity and only be utilized when there is a need to reduce power generation. Additionally, integrating reserves from these sources ensures power system security, reduces dependence on conventional sources, and enhances economic efficiency

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