Model Predictive Control Strategies for Polygeneration systems and microgrids

Increasing electricity and thermal demand in all sectors, an increasing focus on the reduction in carbon emissions and use of nuclear power, advent of distributed generation and greater use of renewable technologies on an aging electrical and thermal grid system has necessitated the need for modern control and management systems. These new control and management systems need to be able to integrate new technologies, stochasticities and maximise the utilisation of the existing infrastructure while satisfying demands, without requiring complete overhaul of the pre-existing centralised grid system and the transmission and distribution systems. A model predictive control system has been proposed and demonstrated here which is able to create strategies for thermal and electrical systems such that the grid efficiency and security is maintained while minimising resource usage and emissions, while, simultaneously reducing the operating costs in the grid. The model predictive control(MPC) utilises a fully energetic approach for low-voltage microgrids and houses in the residential and commercial sector which comprises of CHP units, heat pumps, storage systems(electric and thermal) and stochastic renewable resources, while accounting for the varying dynamics of the electrical and thermal systems. Finally, validation of the MPC is performed on a testbed with physical units and building emulators which have access to meteorological and resource market data. The capability of the MPC to provide strategies for systems with photovoltaics (PV), heat pumps and CHP units is demonstrated. The MPC implementation developed is input into an optimal system design algorithm based on a multi-objective optimisation genetic algorithm developed for microgrids and urban systems/grids with end-users and polygeneration systems and storage devices. The optimal design of the system is so that the optimal sizes of the polygeneration systems can be identified. This will help in maximising the utilisation of heat pumps, storage devices and other systems in a LV microgrid equipped with an MPC-based thermo-electric energy management system. The work also aims to compare the cost effectiveness versus ability of thermal storage devices compared to electrical storage devices for the same grid in question.

Model Predictive Control Strategies for Low-Voltage Microgrids

François Maréchal, Ramanunni Parakkal Menon, Mario Paolone

Modern concepts for grids are being developed owing to the increasing stress put on the electrical producers and the burgeoning need for thermal demands due to varying climate conditions. The increasing awareness of the depletion of natural resources has also been a driver in the direction of modern grids which are more efficient and less susceptible to failures. One solution which is being considered as an alternative for the existing centralised electrical grid and thermal needs is the concept of microgrid. Microgrids are decentralised subunits which consist of different cogeneration units and combined heat and power units along with renewable sources and heat and electricity consumers. Multiple microgrids combine together to aid and complement the existing producers thus, reducing the stress on these producers while increasing the efficiency of the entire electricity grid. The presence of different storage devices and electricity production units mean that the microgrids can also maintain very high quality of electricity, thus reducing losses during transmission and distribution. Another advantage of a microgrid is it's ability of being self-sufficient for a short period time which means that microgrids have the ability to island themselves voluntarily or accidentally from the rest of the electricity grid which could be used to prevent entire grid failures during meteorological problems or when the economic factors are not conducive. The presence of decentralised sources, storage devices and consumers mean that there is a strong need for control of all the different components that constitute a microgrid. There are many different approaches to control a microgrid. This paper attempts to demonstrate a model predictive control (MPC) strategy developed to provide strategies for a low-voltage microgrid with both thermal and electrical devices and requirements. The aim of the strategy is to satisfy the demands and comforts of the consumers while minimising the costs associated by performing a multi-layered mixed-integer optimisation which provides strategy that needs to be employed by each unit of the system. The optimisation consists of two layers, the first of which provides the setpoints for both the thermal and electrical components and the second layer solves a problem which is only takes the electrical units into account and is performed over a shorter timestep. This is owing to the different dynamics of the thermal and electrical units. The fast dynamics of the electrical systems requires us to perform the second layer to maintain the efficiency of the microgrid. The paper then shows the importance of optimal sizing for the model predictive control strategy employed for varying periods of the year and the advantages of this strategy for microgrids over the existing strategies. The paper also attempts to show the impact of certain variables employed in the MPC strategy in particular, for LV (Low-Voltage) microgrid.

Aidic Servizi Srl2014

Urban Energy System Design from the Heat Perspective using mathematical Programming including thermal Storage

Jakob Moritz Fabian Rager

Energy planning recently received more attention in Switzerland through the new strategy phasing out nuclear energy by 2034. Often however the energy planning is only done from the electrical side. This work takes a different angel and helps communities and energy utilities planning tomorrows energy system from a heat based perspective. After the data collection and structuring, the methodology presented here designs an energy system. Based on the quality of the collected data, the approach to define the energy demand should be chosen. In order to reduce calculation time, a data reduction approach is developed to reduce the input data without loosing significant information and precision. In particular, the methodology focuses on the integration of a stochastic resource, in this case solar thermal heat production, in combination with thermal energy storage. The thermal energy storage can be used as a short or long term thermal energy storage. The framework compares design solutions for the two storage types considering either a total cost approach or a life cycle assessment approach using the cumulative exergy demand (CExD). The proposed mathematical programming framework is based on a mixed integer linear programming (MILP) approach, that can work on different levels of detail between building to community or city level. The optimization problem can also be further simplified to a linear problem, increasing the size of problem that can be solved while reducing or keeping a constant computation time. The discussed cases show an interest in further investigating storage solution using both, the short and long term storage at once, because they allow to reduce the system's overall costs or CExD significantly. The framework is then extended to consider buildings as an energy storage. The building's internal temperature can be raised from 20 \degree C up to 23 \degree C, giving a comfort temperature range that can be used for storing heat. The integrating of both storage types, the thermal energy storage and the building as an energy storage, show no significant impact on the energy system design. However, costs or CExD can be reduced. In addition, the heat demand can be modified through the decision of optimal energy retrofitting strategy for a group of buildings. The framework decides which of the building to refurbish based the overall CExD including CExD used for retrofitting the building. Finally, a method is proposed to integrate uncertainty of the model's input parameters into the system design. A global sensitivity analysis evaluates the impact of each uncertain parameter onto the system, allowing to focusing on the outputs of interest. Robust optimization is applied with a simulation-based approach, the additional costs for a robust design are calculated, as well as the different unit sizes. The low complexity of the developed models allows for an easy integration of new data collected during the development of a project, which is often the case in urban energy planning applications.

EPFL2015

SCCER-FURIES - Report on the role of multi-carrier, multi-services, multi-grids integrated energy conversion systems and their possible role in the Swiss energy 2050 - Deliverable D1.5.3

Luc Girardin, François Maréchal, Paul Michael Stadler

The optimisation of distributed and renewable energy systems in buildings has shown that zero energy systems, using existing technologies, is within reach in the household sector where the thermal and the systems can support each other to increase renewable energy harvesting, co-generation and waste heat recovery. Smart and sustainable systems can be deployed rapidly using solar collectors, heat pumps, energy storages and smart power grid driven by a Smart Energy Management strategy (SEM), based on Model Predictive Control (MPC). Targeting energy autonomous systems will indeed require a step further in the development of the infrastructure. In line with the design of future 100% renewable energy systems and autonomous cities, advanced concepts of multi-service energy systems have been proposed. These concepts of multi-carrier, multi-services and multi-grids systems (electricity, gas, heat distribution and mobility) are built around a "smart thermal grid" connecting buildings, centralised plants and distributed heating and cooling producing units, including individual contributions from the connected buildings and Power to Gas storage (P2G). It has been demonstrated that reasonable investments in advanced heat distribution system could lead to substantial benefits for both the economy and individual consumers, with a break-even time shorter than 6 year.