Michele Canelli

Model Predictive Control-Based Optimal Operations of District Heating System With Thermal Energy Storage and Flexible Loads

Operating heating power plant (DHPP) with fluctuating load is a complex problem. Thermal energy storage (TES), flexible loads, and operating constraints compound this complexity further. This investigation focuses on the design of a model predictive controller (MPC) that reduces the operating and maintenance cost in a DHPP, considering TES and flexible loads. The MPC accomplishes this task by scheduling boilers, TES units, and flexible loads. To handle the fluctuating demand, the MPC uses forecasts and combines it with a constrained optimization problem. The objective function reflects the cost, whereas the generator limits, TES dynamics, thermal loads, including supply temperature, power plant layout, and reliability, are the constraints. The resulting optimization problem is modeled as a mixed-integer linear program with both continuous and logic variables. Here the logic variables model the operating modes of the boiler and storage units. The use of receding horizon approach enhances the robustness to the forecast errors. The constraints modeling plant layout, supply temperature, and grid reliability lead to a more realistic solution. The MPC is illustrated using simulation on historical data and experiments on a DHPP at Ylivieska, Finland. Our results demonstrate the cost benefits of the proposed approach.

Optimal operation of a district heating power plant with thermal energy storage

This paper presents an optimal control strategy for a district heating power plant with thermal energy storage. The main goal of the control strategy is to reduce the operation costs of the power plant, by scheduling the boilers, the operation of the thermal energy storage and the curtailment on the loads. The problem is stated as a constrained optimization in the form of a Mixed Integer Linear Program (MILP), embedded on an Model Predictive Control (MPC) framework. Particular attention is paid to modeling of boilers operating constraints, including the outlet water flow temperature, to the energy exchanged with the thermal energy storage and to the operating modes of the power plant layout, including the constraints related to the supply water temperature needed from the network. The results are performed using the data and the layout of the power plant located in the city of Ylivieska, in Finland. The cost analysis performed shows the advantages of using the predictive control strategy.

The Micro-cogeneration and Emission Control and Related Utilization Field

Micro-cogeneration is a developed technology aiming to produce electricity and heat close to the final users, with the potential, if designed and operated correctly, to reduce both the primary energy consumption as well as the associated greenhouse gas emissions when compared to traditional energy supply systems based on separate energy production. The distributed nature of this generation technology has the additional advantages of (i) reducing electrical transmission and distribution losses, (ii) alleviating the peak demands on the central power plants, and (iii) diversifying the electrical energy production, thus improving the security of energy supply. Micro-cogeneration devices are used to meet both electrical requirements and heat demands (for space heating and/or hot water production) of a building; they can be also combined with small-scale thermally fed or mechanically/electrically driven cooling systems. Many micro-cogeneration units are already commercialized in different countries (such as Japan, Germany, United Kingdom, etc.) and in recent years several researches have been carried out in order to advance the design, operation, and analysis of this technology. Currently the use of commercial micro-cogeneration units in applications such as hospitals, leisure facilities, hotels, or institutional buildings is well established. The residential cogeneration industry is in a rapid state of development; the market remains not fully mature, but interest in the technology from manufacturers, energy utilities, and government agencies remains strong.