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Highlights • Framework for using clustering methods in energy systems optimization problems. • Comparison of different clustering methods. • Introduction of shape-based clustering methods to energy systems optimization. • Operational optimization problem: Centroid-based methods represent more predictably. • k -shape improves performance on problems that exploit intra-daily variability. Abstract Modeling time-varying operations in complex energy systems optimization problems is often computationally intractable, and time-series input data are thus often aggregated to representative periods. In this work, we introduce a framework for using clustering methods for this purpose, and we compare both conventionally-used methods (k -means, k -medoids, and hierarchical clustering), and shape-based clustering methods (dynamic time warping barycenter averaging and k -shape). We compare these methods in the domain of the objective function of two example operational optimization problems: battery charge/discharge optimization and gas turbine scheduling, which exhibit characteristics of complex optimization problems. We show that centroid-based clustering methods represent the operational part of the optimization problem more predictably than medoid-based approaches but are biased in objective function estimate. On certain problems that exploit intra-daily variability, such as battery scheduling, we show that k -shape improves performance significantly over conventionally-used clustering methods. Comparing all locally-converged solutions of the clustering methods, we show that a better representation in terms of clustering measure is not necessarily better in terms of objective function value of the optimization problem. [ABSTRACT FROM AUTHOR]

Electricity generated from renewable energy source fluctuates heavily and can hardly be predicted. The peak shaving (or load cycling) operation of conventional thermal power plants is an effective means to mitigate the mismatch between electricity demands and supplies. Therefore, determining how thermal power plants can operate in a flexible and effective mode is an urgent issue that should be addressed. For thermal power plants, new methods and strategies need to be proposed to face the challenge of the integrating flexibility and energy saving into transient processes. Dynamic performances of thermal power plants during load cycling processes are affected by the coupling of the thermal system and the control system. Feasible approaches from optimizing the coordinated control system (CCS) may radically enhance the peak shaving capacity of thermal power plants. The heat storage in a coal-fired power plant, including heating surface metals and work media, varies with the load rate of the plant. During cycling load operations, the real-time heat storage value of one unit differs from that of the corresponding steady state load command rate. This difference hinders the flexibility of one unit and affects its economic performances during cycling processes. In this paper, a revised water fuel ratio (WFR) control strategy based on heat storage difference was proposed and tested on established coal-fired power plant models. Results show that the accumulation deviations of load rate command and real-time load rate are considerably reduced during load cycling processes when the proposed WFR control strategy is introduced. The revised WFR control strategy diminishes the difference between the target and the actual total power output. When the load cycling rate varies from 10 to 30 MW min −1 between 50% and 100% THA, the standard coal consumption variation rate (Δ b s ) decreases by 0.31–1.01 g kW −1  h −1 during loading up processes, and decreases by 0.26–1.69 g kW −1  h −1 during loading down processes. [ABSTRACT FROM AUTHOR]

The heat demand for industrial processes is often provided in the form of steam generated by various fossil fueled equipment. In order to reduce CO 2 emissions, the heat demand has to be covered by renewable energy sources. Electrified steam generation relies on complex energy systems, that can be operated according to energy availability and cost developments. However, such a multi component industrial energy system poses a challenge in modeling and determining the cost- or emission-optimal operation of the system. This study develops a methodology to model a multi component industrial energy system on the basis of a case study. By optimal system operation, either costs or emissions are minimized in response to fluctuating renewable wind energy and electricity prices. A high temperature heat pump (HTHP), a sensible thermal energy storage (TES) and a wind turbine are combined to create an electrified energy system to supply super-heated steam. During periods of low wind speed, additional grid electricity is purchased to ensure a steady heat supply. The HTHP offers a high operational flexibility and thus, enables the charging and discharging of the TES. A model of the closed reverse Brayton cycle HTHP, which is able to simulate part load behavior, is created in a process simulation software and consolidated in nonlinear surrogate models. The component behavior of a TES is represented by a combination of equations based on heat exchanger relations. Finally, the resulting algebraic nonconvex, nonlinear optimization problem based on the proposed system is solved using the local interior point optimizer (IPOPT) solver equipped with a multi-start approach to determine an optimal operation over a reference week with respect to the current wind power generation, grid emissions and electricity prices. The results of the optimization show, that optimal operating strategies enable a high potential to decarbonize future industries at minimum operational costs or emissions. [Display omitted] • Operational optimization of an electrified industrial energy system. • Nonlinear component models. • Novel lumped capacitance model of a sensible thermal energy storage. • Industrial size high temperature heat pump. • System powered by renewable and grid electricity. [ABSTRACT FROM AUTHOR]

With the application of renewable energy in single office buildings, an increasing number of power grids require building systems coupled with renewable energy to realize off-grid operation. However, the uncertainty of renewable energy sources and the safety of the corresponding energy storage equipment have become major challenges for these systems. Reinforcement learning has made considerable progress in the field of building control as an advanced control algorithm; however, research on its application to the off-grid operation of renewable energy systems, particularly the specific reward function design is limited. Therefore, this study considered the off-grid operation of a renewable building energy system and the safety (prevention of deterioration) of its battery as optimization goals. This study is based on a real building energy system, through the operation control of generators, solar photovoltaics, and batteries to achieve optimization purposes. Aiming at these two optimization goals, this study introduces a detailed reward function design and complete reinforcement learning workflow through a Gaussian distribution. Two deep reinforcement learning (DRL) algorithms were trained and verified by offline reinforcement learning based on the measured data-sets of actual existing buildings in Japan. The results show that the proposed reinforcement learning design can better achieve the two optimization goals of off-grid operation and battery safety under ordinary and extreme conditions. On off-grid operation tasks, the best DRL algorithm can achieve a mean hourly grid power purchase error of less than 2 kWh for the entire optimization window. Furthermore, the proposed deep reinforcement learning algorithms can simultaneously maintain a maximum average 7.72 h of battery unsafe state over the entire 168-hour optimization window. • Optimization of off-grid operation of renewable building energy systems using deep reinforcement learning. • We enable DRL algorithms to achieve good results in off-grid operation of renewable building energy systems. • Modeling and algorithm application are carried out based on the measured data of real existing buildings. [ABSTRACT FROM AUTHOR]

In order to realize an energy efficient and emission-free heat and cold supply in urban areas, 5th Generation District Heating and Cooling (5GDHC) networks are a promising technology. In 5GDHC networks, the control of the network temperature is crucial since it affects the efficiency of connected heat pumps and chillers, the heat losses (or gains) of the network, as well as the integration of waste heat or free cooling. Due to the large number of opposing effects, the optimal control of network temperatures is a challenging task. In this paper, a mixed-integer linear program (MILP) is proposed for short-term optimization of the network temperature in 5GDHC systems. The model comprises an air-source heat pump, compression chiller and thermal storage in a central generation unit as well as heat pumps, chillers, electric boilers and thermal storages in buildings. Furthermore, the model considers the thermal inertia of the water mass in the network which functions as additional thermal storage. The optimization model is real-time capable and designed to be deployed in a model-predictive control. In a case study, the optimization approach leads to cost savings in two of three investigated months (by 10 % and 60 % respectively) compared to a reference operation strategy (free floating network temperature). • Operation optimization model for 5th generation district heating and cooling grids. • Network temperature levels are discretized resulting in an MILP formulation. • Performance comparison with free-floating temperature control. • Investigation of storage tank and thermal inertia of network for demand balancing. • Optimization model results in lower costs compared to free-floating temperature. [Display omitted] [ABSTRACT FROM AUTHOR]

• Intermittent heating mode was applied in IHPD systems for wind power integration. • An innovative dual hydraulic parameter calibration method was proposed. • A novel method of optimizing heating start-stop time was developed. • Dynamic hydraulic and thermal conditions of complex DHS were completely simulated. • Effects of intermittent heating mode on IHPD systems were performed. Applying an intermittent heating mode (IHM) in district heating systems (DHSs) during a period of wind power curtailment can effectively decrease the heat demands of the DHSs and increase the operational flexibility of the combined heat and power generation units. Accordingly, it is possible to integrate additional wind power into an integrated heat and power dispatch (IHPD) system. In this study, a new IHPD model with the application of the IHM was built to promote wind power integration. In the IHPD model, to obtain the dynamic hydraulic and thermal conditions of the DHSs and ensure the feasibility of applying the IHM for wind power integration in actual engineering applications, a novel dual hydraulic parameter calibration method was proposed, and an innovative method for determining the optimal heating start-stop times under the IHM was developed. Afterward, the dynamic hydraulic and thermal models of the DHS under the IHM (including corresponding regulation methods) were completely built and embedded into the IHPD model. The effects of the IHM on the IHPD system and optimized hydraulic-thermal characteristics of the DHS were analyzed, based on a real DHS featuring multiple heat sources and looped networks. The results demonstrate that applying the IHM to an IHPD system can significantly increase the operational flexibility of the combined heat and power unit; it can also increase the wind power integration rate from 70.8% to 92.2%, as determined based on a case study. Therefore, the IHM is recommended for application in IHPD systems to promote wind power integration. [ABSTRACT FROM AUTHOR]

• DDRO is firstly applied in energy systems optimization under uncertainty. • Deterministic and data-driven robust optimization frameworks are proposed. • The uncertainty set is constructed by SVC based on real industrial data. • The DDRO model is formulated as an MINLP problem. • The effect of the regularization parameter on the solution is explored. In the large-scale industries, optimization of multi-type energy systems to minimize the total energy cost is of great importance and has received worldwide attentions. In the real industrial plants, the deterministic optimization may encounter difficulties because of various uncertainties. In this paper, the deterministic and robust optimization frameworks are proposed for energy systems optimization under uncertainty. A hybrid modeling method is applied to develop building block models based on the mechanism and process historical data. The deterministic optimization model can be further formulated as a mixed-integer non-linear programming problem. Considering enthalpy uncertainties, a generalized intersection kernel support vector clustering is employed to construct the uncertainty set. By introducing the derived uncertainty set in the deterministic optimization model, a robust optimization model is presented. A case study on the energy system of a real ethylene plant is carried out to illustrate the performance of the proposed approach and the effect of regularization parameter κ on the optimization results is studied. The results show that the optimized energy costs are 15148.84 kg/h and 16209.81 kg/h in deterministic and robust optimization methods. Despite higher energy consumption in robust optimization, the proposed method yields a trade-off between energy cost and robustness. The conservatism of the solution can be adjusted by the regularization parameter, and in this system κ = 0.02 is recommended. [ABSTRACT FROM AUTHOR]