Integrated renewable energy polygeneration networks: a multi-objective optimisation and game theoretic approach - Chitsiga, Takudzwa Brian (Department of Chemical Engineering, 2025)
Renewable energy integration and process optimisation have been employed to address the ever-increasing concerns regarding energy security and pollutant emissions worldwide. Mathematical modelling, paired with game theory principles, can optimize decision-making among stakeholders, enhancing resource allocation and strategic planning. Furthermore, the necessity of industrial symbiosis becomes evident, as it promotes the efficient exchange of resources between different processes, reducing waste and enhancing sustainability. The modelling of these approaches facilitates detailed experimentation without the associated costs and risks of setting up real-life prototypes. In line with this approach, this dissertation presents an integrated resource network model that includes a bioenergy supply chain and a polygeneration hub. The objective is to optimally allocate renewable fuel, and a fossil fuel backup, to the polygeneration hub while optimally distributing the utilities produced to supply industrial and residential demand. The method used involves a 3-layer superstructure. The first layer consists of a supply chain network that includes seasonally available renewable and non-renewable energy sources, linked to the second layer through a transport system made up of railways, roads, and pipelines. The second layer, the polygeneration hub, contains a boiler for generating high-pressure steam, steam turbines for power production, a multi-effect evaporation system for desalinating seawater, and an absorption refrigeration system to produce chilled water. This layer also includes the option of connecting the boiler to a solar-thermal and heat storage subnetwork for preheating boiler feedwater. Piping and electrical cables connect the polygeneration hub to the third layer, which meets industrial heat demand through a heat exchanger network and electrical power demand. The generated model, which is a mixed-integer non-linear program, is applied to two case studies. The first case study provides valuable insights into the impact of weighting factors on cost and emissions, highlighting the model's decision-making process under varying renewable fuel availability and objective preferences. The second case study extends the model's applicability by incorporating cooperative game theory, showcasing its potential to enhance industrial symbiosis and achieve significant cost savings through strategic collaboration. In the first case, a multi-objective function with an equal weighting between total annual cost and CO2 emissions is used. A total annual cost of 40.2 ×106 $/y and emissions of 73.7 × 106 t/y CO2 is obtained. Furthermore, a sensitivity analysis is conducted to determine the effect of changes in biomass availability and weighting factors in the multi-objective function on the solution. The solution reveals that the model prefers to exhaust the supply capacity of the cheapest biomass with lowest carbon composition, before moving on to the next. As a last resort, the model uses fossil fuel to meet the set energy demand. A change in weighting factors in the multi-objective function in favour of economics results in lower total annual cost values, achieved by using cheaper fuel and transport, and reducing number of units in the heat exchanger network and multi-effect evaporation system. When the weighting factor is changed to favour of emission reduction, lower CO2 emission values are obtained by the incorporation of solar thermal energy and increasing the number of units in the heat exchanger network and multi-effect evaporation system. In the second case study, the subnetworks in the superstructure from the first case are treated as individual participants within an industrial symbiosis network, with the potential to engage in a cooperative game. The objective function for this scenario aims to minimise the sum of the marginal contributions of each participant, assigning equal weighting to each, toward the coalition's total annual cost. The results indicate a preference for forming a coalition among the combined heat and power, multi-effect evaporation, and the heat exchanger network. This had the highest cost savings of 465.5 ×105 $/y.