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The average global temperature is rising due to climate change. This leads to an increase in cooling demand along with higher usage of electricity to operate cooling processes. One way to decrease the electricity usage is to introduce absorption cooling which uses heat instead of electricity as its main source of power. To further increase resource efficiency in urban areas centralized district cooling can substitute independent cooling units. In a district cooling network, a mixture of absorption and compressor cooling units, as well as free cooling, can be included. This enables the ability to coordinate which cooling technology is to be used based on profitability at the current time. By introducing an optimization-based plan, the operation of a district cooling network in a smart energy system can incorporate important factors for the interaction between different sectors, such as electricity and district heating prices. The usage of optimization-based tools to plan the operation of energy systems has previously shown promising results. However, further studies are needed to investigate how they perform in different scenarios. There is also a need to develope more reliable forecasts which motivated this study; a case study on the district cooling network "City" in Linköping. The study aimed to develope a method for forecasting the cooling demand in a district cooling network, investigating how the coordination of absorption and compressor cooling units, as well as free cooling, can be improved. This has been done from a system perspective that encompasses the district heating and electricity network by developing an optimization-based operational plan. In this study an explorative method has been used to develope a forecasting tool based on an algorithm and a Mixed Integer Linear Programming (MILP) model with appertaining constraints and coefficients which can solve an Unit Commitment problem for a district cooling network. The forecasting tool and MILP model resulted in an optimization-based operational plan that enabled the ability to coordinate the usage between absorption and compressor cooling units as well as free cooling. The method can be divided into five distinct iterative steps; (1) data collection for the parameters that affect the cooling demand, (2) forecasting of the cooling demand based on the identified parameters, (3) pressure simulations of Linköping's district cooling network in the software NetSim, (4) operational optimization via MILP modeling, and (5) evaluation of the optimization-based operational plan from the perspective of operational cost, electricity and heat usage, as well as greenhouse gas emissions. Six different algorithms were developed to forecast the cooling demand. All of the algorithms were based on the retrospective operation the previous day through linear regressions. The algorithm that best followed a historical operational period on the district cooling network City had a margin of error of 14\%. The algorithm was based on the time of the day and either solar irradiation or outside temperature based on the difference between the forecasted outdoor temperature and the measured temperature the previous day. The MILP model that was developed had an objective function that minimized the operational cost which included the cost of electricity and heat usage, distribution, maintenance, and start-up and shut-down costs. The constraints that was constructed in the MILP model to define a district cooling network included balancing the cooling demand, specifications for the operation of cooling units and distribution flows. Furthermore, the coefficients that defined the City network were estimated dynamically. These included power limitations, operational costs, and start-up costs for each cooling unit, as well as distribution costs for each cooling plant. During this case-study, it was observed that by using optimization-based operational planning produced from a MILP model solving an UC problem, the operational costs, electricity and heat usage can decrease by 27\%, 22\%, and 2\% respectively for this case-study of the City network in Linköping during a seven-month period. In addition, a decrease in greenhouse gases by 16\% was observed when applying the perspective "avoided global emissions". For the calculations an emission factor of 702 $gram \, CO_2-eq/kWh_{el}$ and 130, 72, or 3 $gram \, CO_2-eq/kWh_{heat}$ depending on if waste, bio-oil, or recycled waste wood were used as fuel for the marginal production of district heating. When there was excess heat in the district heating network the emission factor for heat usage was instead assumed to be 0 $gram \, CO_2-eq/kWh_{heat}$. Lastly, this case-study emphasizes the importance of solid operational planning as a foundational pillar in satisfying the increase of future cooling demand in a resource-efficient way for local energy systems in sustainable societies. |
