Model Predictive Control Strategies for Low-Voltage Microgrids

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.

Model Predictive Control Strategies for Polygeneration systems and microgrids

Ramanunni Parakkal Menon

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.

EPFL2017

Thermoeconomic Analysis of a Solar Enhanced Energy Storage Concept Based on Thermodynamic Cycles

Samuel Henchoz

Large scale energy storage may play an increasingly important role in the power generation and distribution sector, especially when large shares of renewable energies will have to be integrated into the electrical grid. Up to now pumped-hydro is the only technology that has been widely used for that purpose. However the spread of this technology is limited by geographic constraints. In the present work, a particular implementation of the storage concept based on thermodynamic cycles, as introduced by ABB Switzerland ltd. Corporate Research, has been analysed thermoeconomically. This concept can be scaled up to a GWh range of stored energy, and is site independent. The underlying idea is to use electricity to power a heat pump transferring heat from a cold source to a hot source (charging mode), while storing the heat and the cold at both sources. Subsequently the energy that has been stored/removed to/from the hot/cold sources can be used to run a heat engine, hence re-delivering the electricity when it is needed (discharging mode). In the implementation presented here, using the ambient temperature as the hot source for both the heat pump and the heat engine allows taking benefit from daily variations of the atmospheric temperature. Indeed, the heat-pump can operate with a higher Coefficient of Performance (CoP) during the night and the heat-engine at higher efficiency during the day. This results in an effective increase of the ratio between the redelivered electricity (discharge) and the stored electricity (charging), compared to a situation where the hot source of both cycles are at the same temperature. The use of solar collectors is a further way to enhance this ratio by increasing the hot source temperature above the ambient temperature during the day. In charging mode, the system consists in an ammonia based Rankine heat pump interfaced at the evaporator with a latent heat cold storage, and at the condenser to the ambient through aircoolers. In discharging mode an ammonia based Rankine heat-engine is interfaced at the condenser with the latent heat cold storage, and the evaporator(boiler) of the thermal engine is interfaced with hot water produced by a field of flat plate solar collectors. A steady state multiobjective optimization of a 50 MW power output plant was carried out, with the objectives of minimizing the investment costs, and maximizing the ratio between electricity output and input. Several types of cold storage substances have been implemented in the formulation and two different types of solar collector were investigated. A storage efficiency of 57% at a cost of 1200 USD/kW was calculated for an optimized plant using solar energy. Finally, a computation of the behaviour of the plant along the year was carried out, reaching a yearly availability of 84.4%.

Pergamon-Elsevier Science Ltd2011

Copper battery for heat to power conversion and energy storage

Sunny Isaïe Maye

An important part of the electricity production relies on heat conversion. Indeed power plants burn fuels like natural gas, coal or use nuclear fission to produce heat that can be transformed into electricity through a thermodynamic cycle and the mechanical work of a turbine. However, with such methods, high efficiencies are only reached with high temperatures according to the Carnot theory. Large amounts of waste heat or low-temperature heat are not converted into electrical power during these processes and are usually simply not exploited. Therefore, there is an opportunity to improve the way in which the heat is used and especially for electricity production, if waste heat can be recovered. Low-grade heat (below 200 â) is available in vast quantities from industry, but also from renewable sources such as solar thermal or geothermal energy. As renewable energies are often available intermittently during specific time of the day or according to the season, the storage of the low-temperature heat coming from these sources is particularly interesting for a more efficient distribution and consumption. However, production of electric power from these heat sources is challenging with existing technologies. Thermally regenerative batteries allow both the conversion and the storage of thermal energy into electric power, but they suffer from low operation voltages and low output powers. Here, we propose a thermally regenerative flow battery based on copper complexation with acetonitrile in non-aqueous solutions operating at voltages above 1V. Cu(I) complex can be destabilized by removal of acetonitrile by distillation, leading to production of solid copper and Cu(II) in a solution of propylene carbonate, thereby charging the battery. With this reaction, we demonstrate the electricity production at power densities up to 200 WÂ·mâ2, and estimate the theoretical efficiency of the full system between 5-14%. The results demonstrate a proof-of-concept for producing and storing electricity from low-quality heat.

EPFL2020