District heating is today the most common way of providing a building with heat and hot water in Sweden. It is an environmentally friendly product mostly used with renewable fuel. However, at power peaks most companies use production units that are more expensive and worse for the environment and should therefore be avoided as much as possible. This can be done with a method called load management. When a power peak occurs, the heat supply to buildings connected to the district heating system can be temporarily reduced. The heat energy can later be returned when the heat demand is lower. Thanks to the heat inertia of the buildings, the indoor temperature will not fall within the time frame for the load management. Historical data has been analysed to identify when and why power peaks occur in the district heating network. Power peaks throughout the district heating network have proved difficult to identify. However, for individual consumers clear patterns of power peaks have emerged. These power peaks mainly occur because of large use of hot water but also because of the shifting outdoor temperature. In order to see how the production cost would differ from the actual outcome load management was applied for Gävle's district heating 2018. The load management was calculated manually by identifying the most expensive production unit on an hourly basis. If a cheaper production unit had the potential to deliver higher power the next hour, the production was moved to the cheaper production unit. The process was repeated for each hour during 2018. After carrying out load management for Gävle's district heating network, 1 457 MWh had been shifted to a cheaper production unit. This resulted in a financial saving of 1,0 % of the total production cost. The environmental savings showed a reduction from 6.1 to 5.9 g CO2eq /kWh a total of 197 tonne CO2eq. In the exact same way, a load management was performed for a scenario where Gävle and Sandviken's district heating network were connected. The gain for a load management with Sandviken will be considerably larger, a reduced production cost of 3.6 % is possible. The environmental savings showed a reduction from 8.4 to 7.8 CO2eq /kWh a total of 575 tonne CO2eq. For future efficient load management, buildings should be divided into different classes depending on the building's time constant. User patterns for the entire district heating network have proved difficult to detect. Artificial intelligence can be an option for short-term forecasting of the power output / Fjärrvärme är idag det vanligaste sättet att förse en bostad med värme och tappvarmvatten i Sverige. Fjärrvärmen är ofta en miljövänlig produkt som kan produceras av till exempel biobränsle- och avfallseldade kraftvärmeverk eller spillvärme från industrier. Vid tillfälligt högt effektbehov, effekttoppar, använder sig merparten av bolagen av dyrare produktionsenheter med större miljöpåverkan. Dyrare produktionsenheter bör undvikas i största möjliga mån och i detta syfte används metoden laststyrning. Vid en effekttopp kan värmetillförseln till byggnader sänkas temporärt för att återföras några timmar senare när effektbehovet är lägre. Tack vare värmetrögheten i byggnaderna bör inomhustemperaturen inte sjunka inom tidsramen för laststyrning. Statistik från Gävles fjärrvärmanvändning på timbasis under 2018 har analyserats för att identifiera när och varför effekttoppar sker. Effekttoppar i hela fjärrvärmenätet har visat sig svåra att identifiera. På lokal nivå har däremot tydliga mönster för effekttoppar framkommit. Dessa effekttoppar beror till största del av tappvarmvattenanvändning men även förändringar i utomhustemperaturen. För att se hur produktion och last kunde skiljt sig från det verkliga utfallet tillämpades laststyrning för Gävles fjärrvärmeproduktion 2018. Laststyrningen beräknades manuellt genom att den dyraste produktionsenheten identifierades på timbasis. Om en billigare produktionsenhet hade potential att leverera högre effekt nästkommande timmar försköts produktionen. Därefter upprepades processen för varje timme under 2018. Efter utförd laststyrning för Gävles fjärrvärmenät hade ca 1 457 MWh förskjutits till en billigare produktionsenhet. Det gav en ekonomisk besparing på 1,0 % av Gävles totala produktionskostnad. Den miljömässiga besparingen visade på en sänkning från 6,1 till 5,9 [g CO2ekv /kWh] sammanlagt 197 ton CO2ekv. På samma sätt utfördes en laststyrning för ett scenario där Gävle och Sandvikens fjärrvärmenät sammankopplats. Vinsten för en laststyrning med Sandviken blev betydligt större med en minskad produktionskostnaden på 3,6 %. Den miljömässiga påverkan sjönk från 8,4 till 7,8 g CO2ekv /kWh sammanlagt 575 ton CO2ekv. För en framtida effektiv laststyrning bör byggnader delas in i olika klasser beroende på byggnadens tidskonstant. Användarmönster för hela fjärrvärmenätet har visat sig svårt att identifiera. Artificiell intelligens kan vara ett alternativ i framtiden för att prognostisera effektuttaget

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.