Predictive control for multi-market trade of aggregated demand response using a black box approach

Aggregated demand response for smart grid services is a growing field of interest especially for market participation. To minimize economic and network instability risks, flexibility characteristics such as shiftable capacity must be known. This is traditionally done using lower level, end user, device specifications. However, with these large numbers, having lower level information, has both privacy and computational limitations. Previous studies have shown that black box forecasting of shiftable capacity, using machine learning techniques, can be done accurately for a homogeneous cluster of heating devices. This paper validates the machine learning model for a heterogeneous virtual power plant. Further it applies this model to a control strategy to offer flexibility on an imbalance market while maintaining day ahead market obligations profitably. It is shown that using a black box approach 89% optimal economic performance is met. Further, by combining profits made on imbalance market and the day ahead costs, the overall monthly electricity costs are reduced 20%

Performance Assessment of Black Box Capacity Forecasting for Multi-Market Trade Application

With the growth of renewable generated electricity in the energy mix, large energy storage and flexible demand, particularly aggregated demand response is becoming a front runner as a new participant in the wholesale energy markets. One of the biggest barriers for the integration of aggregator services into market participation is knowledge of the current and future flexible capacity. To calculate the available flexibility, the current aggregator pilot and simulation implementations use lower level measurements and device specifications. This type of implementation is not scalable due to computational constraints, as well as it could conflict with end user privacy rights. Black box machine learning approaches have been proven to accurately estimate the available capacity of a cluster of heating devices using only aggregated data. This study will investigate the accuracy of this approach when applied to a heterogeneous virtual power plant (VPP). Firstly, a sensitivity analysis of the machine learning model is performed when varying the underlying device makeup of the VPP. Further, the forecasted flexible capacity of a heterogeneous residential VPP was applied to a trade strategy, which maintains a day ahead schedule, as well as offers flexibility to the imbalance market. This performance is then compared when using the same strategy with no capacity forecasting, as well as perfect knowledge. It was shown that at most, the highest average error, regardless of the VPP makeup, was still less than 9%. Further, when applying the forecasted capacity to a trading strategy, 89% of the optimal performance can be met. This resulted in a reduction of monthly costs by approximately 20%

Control of Residential Space Heating for Demand Response Using Grey-box Models

Certain advanced control schemes are capable of making a part of the thermostatic loads of space heating in buildings flexible, thereby enabling buildings to engage in so-called demand response. It has been suggested that this flexible consumption may be a valuable asset in future energy systems where conventional fossil fuel-based energy production have been partially replaced by intermittent energy production from renewable energy sources. Model predictive control (MPC) is a control scheme that relies on a model of the building to predict the future impact on the temperature conditions in the building of both control decisions (space heating) and phenomena outside the influence of the control scheme (e.g. weather conditions). MPC has become one of the most frequently used control schemes in studies investigating the potential for engaging buildings in demand response. While research has indicated MPC to have many useful applications in buildings, several challenges still inhibit its adoption in practice. A significant challenge related to MPC implementation lies in obtaining the required model of the building, which is often derived from measurements of the temperature and heating consumption. Furthermore, studies have indicated that, although demand response in buildings could contribute to the task of balancing supply and demand, suitable tariff structures that incentivize consumers to engage in DR are lacking. The main goal of this work is to contribute with research that addresses these issues. This thesis is divided into two parts.The first part of the thesis explores ways of simplifying the task of obtaining the building model that is required for implementation of MPC. Studies that explore practical ways of obtaining the measurement data needed for model identification are presented together with a study evaluating the suitedness of different low-order model structures that are suited for control-purposes.The second part of the thesis presents research on the potential of utilizing buildings for demand response. First, two studies explore and evaluate suitable incentive mechanisms for demand response by implementing an MPC scheme in a multi-apartment building block. These studies evaluate two proposed incentive mechanisms as well as the impact of building characteristics and MPC scheme implementation. Finally, a methodology for bottom-up modelling of entire urban areas is presented, and proved capable of predicting the aggregated energy demand of urban areas. The models resulting from the methodology are then applied in an analysis on demand response

Ancillary Services 4.0: A Top-To-Bottom Control-Based Approach for Solving Ancillary Services Problems in Smart Grids

Power systems are experiencing a large amount of renewable generation with highly stochastic and partly unpredictable characteristics. This change in energy production implies significant consequences related to the provision of ancillary services (AS). Current markets dedicated to the provision of AS are not able to benefit from the flexible energy resources. They also cannot cope with the new level of stochasticity, non-linearity and dynamics of generation and flexibility. To overcome such issues and exploit the potential of flexibility resources, a new strategy is required. In this paper, by capitalising on flexibility resources’ potential, AS 4.0 approach is proposed, which offers a comprehensive solution for the AS provision in the smart grid era

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

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

Virtual Power Plant for Residential Demand Response

This report presents the progress done in the first 1.5 years in relation to the PhD project “Virtual Power Plant for Residential Demand Response”. The PhD project aims to gain insight on demand response provision in residential buildings by using a Virtual Power Plant (VPP) in a smart grid scenario. The document shows an architectural overview of the designed VPP and a prototype of the latter based on industrial automation equipment. Furthermore, the report introduces the control strategy of the VPP. This strategy is based on a model predictive control approach to locally optimize energy resources and to provide demand response to an aggregator. In addition, an initial study on prediction models for electricity consumption of residential buildings is presented. Load forecasting models like the ones described are used by the intelligence of the VPP for decision making. The report concludes by outlining the future work together with the planed publications that will lead to timely completion of the PhD study

Scaling energy management in buildings with artificial intelligence

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