Energy Flexibility as Additional Energy Source in Multi-Energy Systems with District Cooling

Artificial-neural-network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling

Special issue on “Optimal design and operation of energy systems”

The aim of this paper is to mitigate the problem of high power demand peak and load oscillations in the operation of a large population of thermostatically controlled loads (TCLs) operated by model predictive control (MPC) at the TCL level. Two desynchronized MPC schemes are introduced: 1) adding random delays in reference signals and 2) extra penalizations on MPC objective functions. For characterizing and validating the proposed desynchronization MPC schemes, a partial differential equation (PDE) model is developed to represent the evolution of the operational states of the TCLs controlled by MPC in a population. The focus of this paper is put on the control of cooling fans in server racks of datacenters, whereas the proposed approach is applicable to other types of TCLs. Numerical simulation studies are carried out and the obtained results confirm the validity and the applicability of the developed approach.

A multi-energy system that controls the generation and storage of different types of energy offers great potential for improving the overall system efficiency and operating cost. The economic dispatch problem for multi-energy systems is to find the optimal system configuration that satisfies the demands under a host of constraints. The problem is often formulated as a mixed integer programming problem, which is very hard to solve since the number of variables involved could be in the order of hundreds. The difficulty of the problem is further compounded when model predictive control is implemented because the set points for controlling the multi-energy system at each time step have to be modeled, drastically increasing the number of variables in the optimization problem. In this paper, a large-scale hybrid optimization strategy is proposed to solve the economic dispatch in a multi-energy management system. Mixed-integer linear programming is used to solve a linearly approximated problem to determine the discrete set points followed by nonlinear programming for the continuous variables. We compare different optimization methods for the nonlinear programming problem with respect to the computational time and cost savings. The cost savings is also compared to a rule-based economic dispatch as the baseline. The simulation is performed using the data obtained from an office building in an R modeling and data management environment. We show that the proposed optimization strategy is able to solve the economic dispatch problem of a multi-energy management system with 720 mixed-integer variables within a short timeframe while still accounting for the nonlinearity of the optimization problem.

In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to the grid operation as well as the consumers, such as reducing the stress on the distribution network, minimizing voltage fluctuations and two-way power flows in the distribution network, and reducing the required battery storage capacity for PV integration. This would result in increasing the solar PV generation penetration levels. The aims of this study are twofold. First, spectral (frequency) analyses of solar PV power generation together with the power consumption of multiple building TCLs (such as heating, ventilation, and air conditioning (HVAC) systems, water heaters, and refrigerators) are performed. These analyses define the bandwidth over which these TCLs can operate and also describe the PV generation frequency bandwidth. Such spectral analyses, in frequency domain, can help identify the flexible components of PV generation that can be consumed by the various TCLs through optimal building load utilization. Second, a quadratic optimization problem based on model predictive control is formulated to allow consuming most of the low and medium frequency content of the PV power locally by building TCLs, while maintaining occupants’ comfort and TCLs’ physical constraints. The solution to the proposed optimization problem is achieved using optimal control strategies. Numerical results show that most of the low and medium frequency content of the PV generation can be consumed locally by building TCLs. The remaining high-frequency content of the PV generation can then be stored/offset using energy storage systems.

This study presents an innovative optimization method for resource scheduling in multi-energy storage systems, focusing on improving resource allocation while considering supply-demand flexibility and renewable energy integration. As renewable energy gains popularity and multi-energy systems become more complex, effective utilization of energy storage to achieve supply-demand balance, optimize energy scheduling, and maximize renewable energy integration is crucial. To address this challenge, a Markov dynamic model is developed to capture the dynamic changes in energy supply and demand within the multi-energy storage system. The model is then solved using a reinforcement learning approach to optimize resource scheduling decisions. Numerical simulations and case studies are conducted to validate the effectiveness and feasibility of the proposed method, showcasing its potential to enhance operational efficiency and reliability in multi-energy storage systems amidst constantly changing energy patterns. This research provides valuable insights and decision support for the design and operation of multi-energy storage systems, contributing to the advancement of sustainable energy utilization and promoting sustainable development in the energy sector.

A collaborative matching method for multi-energy supply systems in office buildings considering the random characteristics of electric vehicles

Thermostatically controlled loads (TCLs), which possess thermal inertia, have attracted additional attention for providing ancillary services to power systems. In this regard, the aggregation model for TCLs is critical for evaluating the performance and optimizing the control of a population of TCLs. We learn by theoretical analyses and simulations that the accuracy of the state space model in most cases is deteriorated by applying longer TCL dispatch periods. Thus, a modified aggregated TCL model is proposed and a new state transition matrix is derived in this paper to improve the TCL modeling accuracy for the provision of grid regulation services. The proposed control model for the aggregated TCL accounts for compressor time delays that would enhance the collective TCL behavior. A probabilistic control strategy is presented to reduce the adverse impacts on the durability of TCL compressors and communication requirements for implementing the TCL control signal. Simulation results demonstrate that the proposed aggregated TCL model with control signals can closely follow actual TCL behavior and the proposed control strategy can reduce the number of TCL switching for providing ancillary services to power systems.

This paper proposes an operational planning framework for large-scale thermostatically controlled load (TCL) dispatch. The proposed framework consists of a day-ahead scheduling stage and a real-time operation stage. A thermal comfort model is employed to estimate the occupants' thermal comfort degree. A self-adaptive TCL grouping method is proposed to group the TCLs based on the similarity of the TCL model parameters. Then, a hierarchical day-ahead scheduling model is proposed to make the optimal dispatch plan for the TCL aggregators based on the day-ahead forecasted information. In the real-time operation stage, a predictive control model is proposed for the TCL aggregators to make the real-time TCL dispatch decision based on the updated real-time information. The simulation results prove the efficiency of the proposed framework.

Training and validating algorithms in a simulation testbed can accelerate research and applications of optimal control of residential loads to improve energy flexibility and grid resilience. We developed an open-source simulation environment, AlphaBuilding ResCommunity, that can be used to train and validate algorithms to control a single thermostatically controlled load (TCL) or coordinate a group of TCLs. We used reduced-order models to simulate the thermodynamics of TCLs, and the parameter values were determined from the connected smart thermostat data of real households. The environment was built upon the standardized OpenAI Gym interface. Ancillary functions, such as retrieving the parameters and weather forecasts, are provided to facilitate control strategies that require predictive information. Compared with existing efforts, AlphaBuilding ResCommunity has three advantages: (1) more realistic model settings because the parameter values are identified from actual household operating data, and modelling and measurement uncertainty are considered; (2) passive thermal storage control; and (3) ease of use due to a simple software dependency and standardized interface. We demonstrated the applications of the environment by implementing a Kalman Filter and Model Predictive Control on a single TCL and a Priority-Stack-Based Control and Alternating Direction Method of Multipliers to coordinate multiple TCLs for load tracking.

Rapid increase of the renewable energy share in electricity production requires optimization and flexibility of the power consumption side. Thermostatically controlled loads (TCLs) have a large potential for regulation service provision. Economic model predictive control (MPC) is an advanced control method which can be used to syncronize the power consumption with undispatchable renewable electricity production. Thermal behavior of TCLs can be described by linear models based on energy balance of the system. In some cases, parameters of the model may be time-varying. In this work, we present a modified economic MPC based on linear parameter-varying model. In particular, we provide an exact transformation from a standard economic MPC formulation to a linear program. We assume that the variables influencing the model parameters are known (predictable) for the prediction horizon of the controller. As a case study, we present control system that minimizes operational cost of swimming pool heating system, where parameters of the model depend on the weather forecast. Simulation results demonstrate that the proposed method is able to deal with this kind of systems.

Constant emission factors to assess the carbon footprint of buildings energy use, as usually included in national Building Technical Codes, show their limitations since the electrical grid mix changes constantly. For this reason, hourly-based methods using time-varying penalty signals to calculate carbon emissions and primary energy use in buildings constitute more effective assessment methods, especially with the aim to activate energy flexibility in buildings based on those inputs. Such signals have been developed and tested in the present work. The robustness and effectiveness of the methods is tested throughout two study cases. The first case compares the impact of using hourly signals over constant factors from the standards. For that purpose, a measured aggregated consumption profile corresponding to 226 real households is analyzed. In the second study case, demand response is implemented through control strategies reacting to the hourly penalty signals, aiming to decrease the emissions, primary energy use and cost. Results for the first case reveal that hourly rates better capture the variability of the electric grid compared to constant yearly factors from national standards, with a 50% difference in carbon emissions and a 20% overestimation with primary energy. Results from the second study case show how the implemented modulation strategies offer benefits in the flexible scenarios compared to the base scenarios, in terms of accumulated emissions or primary energy. Improvements are especially perceived when splitting data seasonally and considering periods with higher demand. Furthermore, this study provides insights for developing energy flexibility inputs when assessing the building performance during critical events such as the COVID19 pandemic or extreme weather conditions, where hourly and seasonal variation might have greater impact. Demand response mechanisms as energy flexibility strategies studied through this work might help in the reduction of total emissions and primary energy. Depending if the goal is to shift the demand due to environmental or economical reasons, different modulation strategies can be implemented to reach greater benefits.

Smart controls for heat pumps are required to harness the full energy flexibility potential of building thermal loads. A literature review revealed that most strategies used for this purpose can be classified in two categories: simpler rule-based control (RBC), and model predictive control (MPC), a more complex strategy based on optimization and requiring a prior model of the systems. Both RBC and MPC can use external penalty signals to prompt their actions. The price of electricity is most often used for this purpose, leading to strategies of cost reduction. As an alternative penalty signal, a novel marginal CO2 emissions signals was also conceived. In this thesis, both an RBC and an MPC controllers were developed as supervisory controls for an air-to-water heat pump supplying the heating and cooling needs of a residential building type from the Mediterranean area of Spain. The RBC strategy modulates the temperature set-points, while the MPC strategy minimizes the overall summed penalties (costs or emissions) due to the heat pump use, while balancing with comfort constraints and a proper operation of the systems. The MPC controller in particular required the development of a simplified model of the building envelope and of the heat pump performance, both adjusted differently for heating or cooling. The MPC included several novelties, such as the mixed-integer formulation, the heat pump simplified model based on experimental data and the consideration of its computational delay. The developed controllers were then tested, firstly in an experimental “hardware-in-the-loop” setup, with a real heat pump installed in the laboratory facilities, and connected to thermal benches that emulated the loads from a building model. Implementing the control strategies on a real heat pump enabled to highlight some practical challenges such as model mismatch in the MPC, communication issues, interfacing and control conflicts with the heat pump local controller. Secondly, a simulation-only framework was developed to test other configurations of the controllers, with TRNSYS as the main dynamic building simulation tool, coupled with MATLAB for the MPC controller. In that case, the real heat pump was replaced by a detailed model which was specially developed for this purpose. It is based on static tests performed in the laboratory, and therefore reproduces the dynamic behavior of the heat pump with high fidelity. The results from experimental and simulation studies revealed the ability of both types of controllers to shift the building loads towards periods of cheaper or less CO2-emitting electricity, these two objectives being in fact contradictory. In the cases where the reference control presented a large margin for improvements, the RBC and MPC controllers performed equally and provided important savings: around 15% emissions savings in heating mode, and 30% cost savings in cooling mode. In the cases where the reference control already performed close to optimally, the RBC controller failed to provide improvements, while the MPC benefitted from its stronger optimization and prediction features, reaching 5% cost savings in heating mode and 10% emissions savings in cooling mode. The research carried out in this thesis covered many aspects of energy flexibility in buildings: creation of input penalty signals, graphical representation of flexibility, development of controllers, performance in realistic experimental setup, fitting of appropriate models and compared performance in heating and cooling. The development efforts and barriers hindering the deployment of MPC controllers at large scale for building climate control have additionally been discussed. The performance of the developed controllers was evidenced in the thesis, proving their potential for load-shifting incentivized by different penalty signals: they could become a strong asset to unlock demand-side flexibility and in fine, help integrating a larger share of RES in the grid.

Demand response can provide services to the power network, however, coordination of spatially distributed demand response resources generally requires coping with imperfect communication networks. This work investigates methods to manage communication constraints (e.g., Delays and bandwidth limitations), faced by demand response aggregators who manipulate the on/off modes of residential thermostatically controlled loads (TCLs). We present two model predictive control (MPC) algorithms that exploit a priori knowledge of delay statistics. We also present three Kalman filter-based state estimation methods that handle measurements with heterogeneous delays that are known a posteriori. We simulate the closed loop system to quantify the error while the system tracks simplified power system signals of various frequencies. We find that the MPC algorithm incorporating the full delay distribution, versus only the mean delay, reduces the average tracking error 39%. Also, incorporating individual TCL models, identified on-line, within the state estimator versus only using a TCL aggregation model reduces the average estimation error 19%.

Energy flexibility of buildings has the potential to support deeper and more efficient decarbonisation of electricity systems, especially if aspirations to electrify heating become true. A promising technology to realise the flexibility potential in buildings is model-predictive control (MPC), where a model of a building and forecasts are used to determine optimal control decisions. This research aimed to evaluate the ability of a model-predictive space heating control strategy to use building thermal mass in delivery of energy flexibility for demand response programmes.The Aim was achieved by developing versions of a MPC strategy for delivery of flexibility and implementing it in a simulation and physical environment. The simulation was a community-scale co-simulation of thirty dwellings, which was used to explore the innate factors that affected the delivery of a fixed demand reduction with the MPC. The physical environment was a matched pair test house facility. Experiments in the test houses were used to address the elements that the simulation lacked: the real-life uncertainties and their effects on the delivery of flexibility.The results showed that the delivery of flexibility was dependent on the weather, physical features of the buildings, the control architecture and market environment. In both simulations and experiments, moderate weather conditions improved the flexibility potential. Weather also had an impact on the baseline error and thus its uncertainty. Weather variables not included in the MPC predictions, like solar irradiation or wind speeds, tended to cause errors in the MPC predictions. Elements impacting energy efficiency were found to affect the ability to deliver demand reductions. The houses in the simulations and experiments were typical English dwellings with limited levels of insulation and air-tightness, which led to relatively rapid changes in indoor air temperatures, on average 2.0-2.3\textdegree C in 40-50 minutes, when heating was turned off in the test house experiments. The control architecture and implementation was found to affect the overall optimality of control. In the simulations, this led to a 44.9\% increase in peak power, when the centralised strategy with knowledge of all buildings was changed to a decentralised MPC. This was because only some of the houses in the community were used to deliver the demand reduction. The market environment affected the response of the MPC in two ways. First, the choice of the baseline was shown to be important, especially when considering uncertainty. The MPC projections were found to have the lowest uncertainty out of the baseline scenarios included in the analysis. For a day in January, the estimated uncertainty in 10-minute mean power was 23.8~\% of the total heater capacity. Second, varying the pricing in the MPC objective function had effects on the response pattern. Either the MPC performed a load-shifting pattern, or it shed load, which were two very distinct response strategies.