Towards Optimal Sustainable Energy Systems in Nordic Municipalities

Abstract

Municipal energy systems in the northern regions of Finland, Norway, and Sweden face multiple challenges: large-scale industries, cold climate, and a high share of electric heating characterize energy consumption and cause significant peak electricity demand. Local authorities are committed in contributing to national goals on CO2 emission reductions by improving energy efficiency and investing in local renewable electricity generation, while considering their own objectives for economic development, increased energy self-sufficiency, and affordable energy costs. This paper formulates a multi-objective optimization problem about these goals that is solved by interfacing the energy systems simulation tool EnergyPLAN with a multi-objective evolutionary algorithm implemented in Matlab. A sensitivity analysis on some key economic parameters is also performed. In this way, optimal alternatives are identified for the integrated electricity and heating sectors and valuable insights are offered to decision-makers in local authorities. Piteå (Norrbotten, Sweden) is used as a case study that is representative of Nordic municipalities, and results show that CO2 emissions can be reduced by 60% without a considerable increase in total costs and that peak electricity import can be reduced by a maximum of 38%.

How spatial policies can leverage energy transitions − Finding Pareto-optimal solutions for wind turbine locations with evolutionary multi-objective optimization

“…GAs are capable of generating discontinuous or concave Pareto Fronts including spatial dependencies of optimization objectives (Song and Chen, 2018a). Therefore, GAs have been used for various spatial allocation problems such as water resource management (Sahebgharani, 2016), land use planning (Schwaab et al, 2017) or energy system planning (Fischer et al, 2020;Kachirayil et al, 2022). In order to better understand trade-offs of planning olicies in multi-objective spatial allocation problems, Seppelt et al (2013) combine MO optimization with scenario approaches.…”

Reviewing local and integrated energy system models: insights into flexibility and robustness challenges

“…Multi-objective optimization is a method to directly assess trade-offs between at least two different objectives, most frequently cost and CO 2 emissions as Fig. 4 illustrates [143][144][145][146]. Notable exceptions also consider aspects such as primary energy consumption [90], selfgeneration [147,148] or fuel consumption [149,150].…”

“…The most common type of uncertainty assessment in the study sample is scenario analysis, which was included in over 80% of all studies. Sensitivity analyses were the second most common option, used by a quarter of studies and applied to cost assumptions [48,79,90,143,153], technology availability [117,122,139,154,155], or both [58,93,103,132,156]. Beyond these, more sophisticated mathematical formulations such as interval linear programming or chanceconstrained programming can also be used to generate more resilient solutions [56,[68][69][70]80,83], but are significantly rarer due to the required effort.…”

Optimal Sustainable Transport Solutions Integrated into a Nordic Municipal Energy System

“…A case study with a focus on electricity and heating sectors for the Piteå municipal energy system was implemented in EnergyPLAN as part of the INTERREG project Arctic Energy between 2016 and 2018 [85]. Based on this case study optimal solutions for the integration of electricity and heating sectors have been investigated in [86]. This paper builds on that work and includes the transport sector as well.…”