Economic model predictive control for optimal operation of combined heat and power systems

© 2019. ElsevierThe use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Hence, to determine the optimal operation of these systems in the changing energy market, the time-varying price profiles for both electricity as well as the required resources and the energy-market constraints should be considered into the design of the control strategies. To solve these issues and maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller, which will be designed according to the Economic Model Predictive Control (EMPC) approach. The proposed controller is designed considering a non-constant time step to get a high sampling frequency for the near instants and a lower resolution for the far instants. Besides, a soft constraint to met the market constraints for the sale of electric power is proposed. The proposed controller is developed based on a real CHP plant installed in the ETA research factory in Darmstadt, Germany. Simulation results show that lower computational time can be achieved if a non-constant step time is implemented while the market constraints are satisfied.Peer ReviewedPostprint (author's final draft

Optimal operation of combined heat and power systems: an optimization-based control strategy

The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany.Peer ReviewedPostprint (author's final draft

District heating energy generation optimisation considering thermal storage

Modern, decentralised, multi-energy vector districts have great potential to reduce energy consumption and emissions. However, due to the complex nature of these systems, they require intelligent management to maximise their benefit. Therefore, this paper models the energy generation of a district heating plant for the purpose of hourly, operational optimisation. Crucially, non-linear, part-load efficiency curves, and minimum load percentages are included in the energy generation modelling as well as thermal energy storage. Due to the non-linearities, a genetic algorithm, optimisation approach was utilised. The optimisation framework was applied to a case study district with three distinct thermal energy generation sources, a gas CHP, gas boilers, and biomass boilers. The optimisation controlled the load percentage of each technology as well as varying thermal storage capacity to minimise the cost of meeting the heat demand. The study found that compared to the current, rule-based approach, the optimisation achieved a significant cost saving of 12.7% without any thermal storage. As the thermal storage capacity was increased the potential cost saving was also shown to increase proportionally to 22.6% with 1000 kWh of storage

Upscaling energy control from building to districts: current limitations and future perspectives

Due to the complexity and increasing decentralisation of the energy infrastructure, as well as growing penetration of renewable generation and proliferation of energy prosumers, the way in which energy consumption in buildings is managed must change. Buildings need to be considered as active participants in a complex and wider district-level energy landscape. To achieve this, the authors argue the need for a new generation of energy control systems capable of adapting to near real-time environmental conditions while maximising the use of renewables and minimising energy demand within a district environment. This will be enabled by cloud-based demand-response strategies through advanced data analytics and optimisation, underpinned by semantic data models as demonstrated by the Computational Urban Sustainability Platform, CUSP, prototype presented in this paper. The growing popularity of time of use tariffs and smart, IoT connected devices offer opportunities for Energy Service Companies, ESCo’s, to play a significant role in this new energy landscape. They could provide energy management and cost savings for adaptable users, while meeting energy and CO2 reduction targets. The paper provides a critical review and agenda setting perspective for energy management in buildings and beyond

Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm

Decentralisation of energy generation and distribution to local districts or microgrids is viewed as an important strategy to increase energy efficiency, incorporate more small-scale renewable sources and reduce greenhouse gas emissions. To achieve these goals, an intelligent, context-aware, adaptive energy management platform is required. This paper will demonstrate two district energy management optimisation strategies; one that optimises district heat generation from a multi-vector energy centre and a second that directly controls building demand via the heating set point temperature in addition to the heat generation. Several Artificial Neural Networks are used to predict variables such as building demand, solar photovoltaic generation, and indoor temperature. These predictions are utilised within a Genetic Algorithm to determine the optimal operating schedules of the heat generation equipment, thermal storage, and the heating set point temperature. Optimising the generation of heat for the district led to a 44.88% increase in profit compared to a rule-based, priority order baseline strategy. An additional 8.04% increase in profit was achieved when the optimisation could also directly control a proportion of building demand. These results demonstrates the potential gain when energy can be managed in a more holistic manner considering multiple energy vectors as well as both supply and demand

Supervisory control design for microgrids energy management optimization based on renewable generation and consumption forecasting

Solar-based electricity production has become an essential part of the general energy production in the recent years with the will to use more renewable sources. The one issue that appears is the uncertainty of the solar irradiation. It is then more complicated to predict the energy generated in the future times. The Energy Management System used on the grid schedules the energy exchanges between the devices based on the prediction of the state of the system in the next time interval. The Model Predictive Control forecasts the power produced as well as that of the energy demand from the load and defines the state of the system. In order to minimize the corresponding cost function, this forecast should be as accurate as possible, with the minimum prediction error. To address these forecasting needs, we will extract some data from a database using an algorithm directly connected to the server. And we will compute the remaining values using an accurate forecasting method, the Simple Average. Then, for this information to be even more precise, we use the Rolling Horizon approach, that enables a regular updating of the forecast. Simulation results and experiments confirm the influence of some parameters on the prediction error and hence on the cost function

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted

Energy management for user’s thermal and power needs:A survey

The increasing world energy consumption, the diversity in energy sources, and the pressing environmental goals have made the energy supply–demand balance a major challenge. Additionally, as reducing energy costs is a crucial target in the short term, while sustainability is essential in the long term, the challenge is twofold and contains clashing goals. A more sustainable system and end-users’ behavior can be promoted by offering economic incentives to manage energy use, while saving on energy bills. In this paper, we survey the state-of-the-art in energy management systems for operation scheduling of distributed energy resources and satisfying end-user’s electrical and thermal demands. We address questions such as: how can the energy management problem be formulated? Which are the most common optimization methods and how to deal with forecast uncertainties? Quantitatively, what kind of improvements can be obtained? We provide a novel overview of concepts, models, techniques, and potential economic and emission savings to enhance energy management systems design