A service reliability model predictive control with dynamic safety stocks and actuators health monitoring for drinking water networks

This paper presents a model predictive control strategy to assure reliability in drinking water networks given a customer service level and a forecasting demand. The underlying idea concerns a two-layer hierarchical control structure. The upper layer performs a local steady-state optimization to set up an inventory replenishment policy based on dynamic safety stocks for each tank in the network. At the same stage, actuators health is revised to set up their next maximum allowable degradation in order to efficiently distribute overall control effort and guarantee system availability. In the lower layer, a model predictive control algorithm is implemented to compute optimal control set-points to minimize a multiobjective cost function. Simulation results in the Barcelona drinking water network have shown the effectiveness of the dynamic safety stocks allocation and the actuators health monitoring to assure service reliability and optimizing network operational costs.Peer ReviewedPostprint (author’s final draft

Advanced control strategies for optimal operation of a combined solar and heat pump system

The UK domestic sector accounts for more than a quarter of total energy use. This energy use can be reduced through more efficient building operations. The energy efficiency can be improved through better control of heating in houses, which account for a large portion of total energy consumption. The energy consumption can be lowered by using renewable energy systems, which will also help the UK government to meet its targets towards reduction in carbon emissions and generation of clean energy. Building control has gained considerable interest from researchers and much improved ways of control strategies for heating and hot water systems have been investigated. This intensified research is because heating systems represent a significant share of our primary energy consumption to meet thermal comfort and indoor air quality criteria. Advances in computing control and research in advanced control theory have made it possible to implement advanced controllers in building control applications. Heating control system is a difficult problem because of the non-linearities in the system and the wide range of operating conditions under which the system must function. A model of a two zone building was developed in this research to assess the performance of different control strategies. Two conventional (On-Off and proportional integral controllers) and one advanced control strategies (model predictive controller) were applied to a solar heating system combined with a heat pump. The building was modelled by using a lumped approach and different methods were deployed to obtain a suitable model for an air source heat pump. The control objectives were to reduce electricity costs by optimizing the operation of the heat pump, integrating the available solar energy, shifting electricity consumption to the cheaper night-time tariff and providing better thermal comfort to the occupants. Different climatic conditions were simulated to test the mentioned controllers. Both on-off and PI controllers were able to maintain the tank and room temperatures to the desired set-point temperatures however they did not make use of night-time electricity. PI controller and Model Predictive Controller (MPC) based on thermal comfort are developed in this thesis. Predicted mean vote (PMV) was used for controlling purposes and it was modelled by using room air and radiant temperatures as the varying parameters while assuming other parameters as constants. The MPC dealt well with the disturbances and occupancy patterns. Heat energy was also stored into the fabric by using lower night-time electricity tariffs. This research also investigated the issue of model mismatch and its effect on the prediction results of MPC. MPC performed well when there was no mismatch in the MPC model and simulation model but it struggled when there was a mismatch. A genetic algorithm (GA) known as a non-dominated sorting genetic algorithm (NSGA II) was used to solve two different objective functions, and the mixed objective from the application domain led to slightly superior results. Overall results showed that the MPC performed best by providing better thermal comfort, consuming less electric energy and making better use of cheap night-time electricity by load shifting and storing heat energy in the heating tank. The energy cost was reduced after using the model predictive controller

Forecasting of process disturbances using k-nearest neighbours, with an application in process control

This paper examines the prediction of disturbances based on their past measurements using k-nearest neighbours. The aim is to provide a prediction of a measured disturbance to a controller, in order to improve the feed-forward action. This prediction method works in an unsupervised way, it is robust against changes of the characteristics of the disturbance, and its functioning is simple and transparent. The method is tested on data from industrial process plants and compared with predictions from an autoregressive model. A qualitative as well as a quantitative method for analysing the predictability of the time series is provided. As an example, the method is implemented in an MPC framework to control a simple benchmark model

Review of control strategies for improving the energy flexibility provided by heat pump systems in buildings

Peer ReviewedPostprint (author's final draft

Control of Energy Storage

Energy storage can provide numerous beneficial services and cost savings within the electricity grid, especially when facing future challenges like renewable and electric vehicle (EV) integration. Public bodies, private companies and individuals are deploying storage facilities for several purposes, including arbitrage, grid support, renewable generation, and demand-side management. Storage deployment can therefore yield benefits like reduced frequency fluctuation, better asset utilisation and more predictable power profiles. Such uses of energy storage can reduce the cost of energy, reduce the strain on the grid, reduce the environmental impact of energy use, and prepare the network for future challenges. This Special Issue of Energies explore the latest developments in the control of energy storage in support of the wider energy network, and focus on the control of storage rather than the storage technology itself

Dynamic optimization of energy systems with thermal energy storage

textThermal energy storage (TES), the storage of heat or cooling, is a cost-effective energy storage technology that can greatly enhance the performance of the energy systems with which it interacts. TES acts as a buffer between transient supply and demand of energy. In solar thermal systems, TES enables the power output of the plant to be effectively regulated, despite fluctuating solar irradiance. In district energy systems, TES can be used to shift loads, allowing the system to avoid or take advantage of peak energy prices. The benefit of TES, however, can be significantly enhanced by dynamically optimizing the complete energy system. The ability of TES to shift loads gives the system newfound degrees of freedom which can be exploited to yield optimal performance. In the hybrid solar thermal/fossil fuel system explored in this work, the use of TES enables the system to extract nearly 50% more solar energy when the system is optimized. This requires relaxing some constraints, such as fixed temperature and power control, and dynamically optimizing the over a one-day time horizon. In a district cooling system, TES can help equipment to run more efficiently, by shifting cooling loads, not only between chillers, but temporally, allowing the system to take advantage of the most efficient times for running this equipment. This work also highlights the use of TES in a district energy system, where heat, cooling and electrical power are generated from central locations. Shifting the cooling load frees up electrical generation capacity, which is used to sell power to the grid at peak prices. The combination of optimization, TES, and participation in the electricity market yields a 16% cost savings. The problems encountered in this work require modeling a diverse range of systems including the TES, the solar power plant, boilers, gas and steam turbines, heat recovery equipment, chillers, and pumps. These problems also require novel solution methods that are efficient and effective at obtaining workable solutions. A simultaneous solution method is used for optimizing the solar power plant, while a static/dynamic decoupling method is used for the district energy system.Chemical Engineerin

Algoritmos adaptativos para controlo eficiente de bombas numa rede de distribuição de água

Modern cities have water distribution networks (WDN) that reliably meet the water demand of every individual household. These networks rely on pumps to move water from distant sources to the consumer. If inefficiently managed, this energy consuming process can become very costly. Using emerging computer power, simulation technologies and sensing devices, many techniques were developed that produce the most cost-efficient operational strategies. These methodologies rely on water consumption predictions to provide optimal pumping strategies. However, these predictions always contain errors, which may create control problems. Many efforts were made to develop progressively better predictions, some of which can change its parameters in real time. Nevertheless, these solutions still contain errors and cannot adapt appropriately to sudden changes in demand. To solve this problem, an adaptive controller that can efficiently update the pumping strategy based on monitored deviations of the predicted consumption is proposed. This methodology takes into account the tariffs of electricity over time and an optimal reference to continuously make the most cost-efficient updates in the pumping strategy. To validate the adaptive controller, two case studies were used: (i) a simple pump-reservoir network and (ii) the Richmond benchmark network. Both evaluations delivered positive results achieving the desired reliability while improving cost efficiency. The combination water consumption predictions and adaptive control methodologies provide a reliable and cost-efficient solution to operate a WDN automatically. This control model may become an essential feature in emerging water grids technology by closing the loop in the control system.Cidades modernas têm redes de distribuição de água que de forma confiável satisfazem as necessidades de água de todos os domicílios. Estas redes dependem de bombas para mover a água de fontes distantes ao consumidor. Se gerido de forma ineficiente, este processo consumidor de energia pode tornar-se muito dispendioso. Através de poder computacional emergente, tecnologias de simulação e sensores, muitas técnicas foram desenvolvidas para produzir as melhores estratégias de operação. Estas metodologias dependem de previsões do consumo de água para criar estratégias de bombeamento óptimas. Porém, estas previsões contêm sempre erros, que podem criar problemas de controlo. Muitos esforços foram feitos para desenvolver melhores previsões, algumas das quais podem mudar os seus parâmetros em tempo real. De qualquer forma, estas soluções ainda contem erros e não conseguem adaptar a mudanças drásticas de consumo. Para resolver este problema é proposto um controlo adaptativo capaz de eficientemente actualizar em tempo real a estratégia de bombeamento com base nos desvios monitorizados relativamente à previsão. Esta metodologia toma em consideração uma referência optimizada para continuamente fazer as actualizações mais eficientes à estratégia de bombeamento. Para validar o controlo adaptativo, dois casos de estudo foram explorados: (i) uma rede simples composta de uma bomba e um tanque, (ii) e a de rede de referência de Richmond. Ambas as avaliações entregaram resultados positivos melhorando a eficiência a nível de custo. A combinação de previsões de consumo de água com metodologias de controlo adaptativo providencia uma solução confiável e eficiente para controlo automático de redes de distribuição de água. Este modelo de controlo pode se tornar uma característica essencial na tecnologia emergente “water grids” fechando o ciclo de controlo no sistema.Mestrado em Engenharia Informátic