Data-driven Demand Response Modeling and Control of Buildings with Gaussian Processes

This paper presents an approach to provide demand response services with buildings. Each building receives a normalized signal that tells it to increase or decrease its power demand, and the building is free to implement any suitable strategy to follow the command, most likely by changing some of its setpoints. Due to this freedom, the proposed approach lowers the barrier for any buildings equipped with a reasonably functional building management system to participate in the scheme. The response of the buildings to the control signal is modeled by a Gaussian Process, which can predict the power demand of the buildings and also provide a measure of its confidence in the prediction. A battery is included in the system to compensate for this uncertainty and improve the demand response performance of the system. A model predictive controller is developed to optimally control the buildings and the battery, while ensuring their operational constraints with high probability. Our approach is validated by realistic co-simulations between Matlab and the building energy simulator EnergyPlus

Review of techniques to enable community-scale demand response strategy design

Incorporating demand side flexibility can aid in integrating intermittent renewable energy generation and reducing the electricity grid’s operational costs. Buildings have the potential to provide demand response (DR) with minimal disruption to activities by leveraging the inherent energy storage in their heating ventilation and air conditioning (HVAC) systems. Harnessing this flexibility whilst minimising energy consumption and maintaining thermal comfort requires control strategies capable of incorporating these objectives, making model-predictive control (MPC) a promising framework. To elucidate the control techniques available to harness the HVAC flexibility of collections of buildings to participate in electricity markets, this paper reviews the current state of literature describing MPC techniques for community-scale control. The reviewed studies were classified based the following characteristics: the general aim of the MPC approach, objective function, thermal response model, amount and type of buildings considered, DR type, control structure, solving tools and techniques, and the energy, cost savings or flexibility achieved. The review shows that MPC strategies can successfully provide many types of DR indicating the versatility of the control approach. Decentralised control approaches reduced the complexity of the large-scale control problem whilst providing more autonomy to individual users. However, compared to centralised approaches, decentralised control led to lower amounts of flexibility. Lastly, few studies validated the performance of their controller in either simulation or physical environments. Therefore, the review suggests further research is needed to study and validate the performance the of different MPC control structures considering various community types and concurrent participation in various DR schemes

COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibility

Demand flexibility is increasingly important for power grids. Careful coordination of thermostatically controlled loads (TCLs) can modulate energy demand, decrease operating costs, and increase grid resiliency. We propose a novel distributed control framework for the Coordination Of HeterOgeneous Residential Thermostatically controlled loads (COHORT). COHORT is a practical, scalable, and versatile solution that coordinates a population of TCLs to jointly optimize a grid-level objective, while satisfying each TCL's end-use requirements and operational constraints. To achieve that, we decompose the grid-scale problem into subproblems and coordinate their solutions to find the global optimum using the alternating direction method of multipliers (ADMM). The TCLs' local problems are distributed to and computed in parallel at each TCL, making COHORT highly scalable and privacy-preserving. While each TCL poses combinatorial and non-convex constraints, we characterize these constraints as a convex set through relaxation, thereby making COHORT computationally viable over long planning horizons. After coordination, each TCL is responsible for its own control and tracks the agreed-upon power trajectory with its preferred strategy. In this work, we translate continuous power back to discrete on/off actuation, using pulse width modulation. COHORT is generalizable to a wide range of grid objectives, which we demonstrate through three distinct use cases: generation following, minimizing ramping, and peak load curtailment. In a notable experiment, we validated our approach through a hardware-in-the-loop simulation, including a real-world air conditioner (AC) controlled via a smart thermostat, and simulated instances of ACs modeled after real-world data traces. During the 15-day experimental period, COHORT reduced daily peak loads by an average of 12.5% and maintained comfortable temperatures.Comment: Accepted to ACM BuildSys 2020; 10 page

Contracted energy flexibility characteristics of communities: Analysis of a control strategy for demand response

Increasing energy system flexibility through demand-side measures will help meet challenges brought by the transition to a low-carbon energy system. Through participation in demand response programmes, buildings can act as sources of contracted flexibility. Contracted flexibility, in this work, is defined as energy flexibility that is supplied to fulfil a set of contractual terms that define when and how demand modifications are delivered and under which incentives or penalties. This paper identifies the factors affecting contracted energy flexibility potential of homes implemented with a model-predictive control strategy designed to deliver a simplified but yet generalisable incentive-based demand response scheme. The control strategy was implemented in centralised and naive-decentralised architectures using co-simulations to observe interaction of the controller with an English community of 30 homes fitted with air-source heat pumps. The results showed that the control strategy was able to deliver sustained demand reductions without violating comfort by preheating the homes prior to demand response periods, if conditions were suitable. Preheating the homes increased overall energy consumption and, in some cases, caused a peak in electricity demand prior to the DR period. Modifying factors of control operation, like the coordination strategy, magnitudes of penalties, control constraints and notice period between call for demand reduction and its delivery, were shown to affect the ability to deliver demand reductions. The contracted flexibility potential of the community was shown to be characterised by the buildings and their systems, the physical and contractual environment, and behaviour and preferences of the occupants

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Data-driven modelling for demand response from large consumer energy assets

Demand response (DR) is one of the integral mechanisms of today’s smart grids. It enables consumer energy assets such as flexible loads, standby generators and storage systems to add value to the grid by providing cost-effective flexibility. With increasing renewable generation and impending electric vehicle deployment, there is a critical need for large volumes of reliable and responsive flexibility through DR. This poses a new challenge for the electricity sector. Smart grid development has resulted in the availability of large amounts of data from different physical segments of the grid such as generation, transmission, distribution and consumption. For instance, smart meter data carrying valuable information is increasingly available from the consumers. Parallel to this, the domain of data analytics and machine learning (ML) is making immense progress. Data-driven modelling based on ML algorithms offers new opportunities to utilise the smart grid data and address the DR challenge. The thesis demonstrates the use of data-driven models for enhancing DR from large consumers such as commercial and industrial (C&I) buildings. A reliable, computationally efficient, cost-effective and deployable data-driven model is developed for large consumer building load estimation. The selection of data pre-processing and model development methods are guided by these design criteria. Based on this model, DR operational tasks such as capacity scheduling, performance evaluation and reliable operation are demonstrated for consumer energy assets such as flexible loads, standby generators and storage systems. Case studies are designed based on the frameworks of ongoing DR programs in different electricity markets. In these contexts, data-driven modelling shows substantial improvement over the conventional models and promises more automation in DR operations. The thesis also conceptualises an emissions-based DR program based on emissions intensity data and consumer load flexibility to demonstrate the use of smart grid data in encouraging renewable energy consumption. Going forward, the thesis advocates data-informed thinking for utilising smart grid data towards solving problems faced by the electricity sector