Sustainability has gained considerable interest at all levels in society during the last decades. Climate change has been a large contributor to this mentality change. As current energy systems are inefficient and contribute to climate change, a transition towards more efficient energy systems is sought. The share of local produced energy in the total energy mix increases, for example through small scale solar installations at household level. Integrated Community Energy Systems (ICES) is a community energy concept that looks into the optimal integration of distributed energy sources and engage local communities, as a solution to the drawbacks of the current energy system. These drawbacks include: notable transmission losses, significant carbon emission, flexibility limitations and a primarily one-dimensional structural design. ICES deals with these drawbacks and allows energy exchange between community members. This multi-source multi-product energy network framework is a broad and flexible concept that involves all facets of energy within a community. Within this research, focus lies on household level energy demand and production, although ICES is able to cover a wider variety of energy forms and carriers. This research uses a bottom-up approach and analyses different demand profiles at household level and a selection of available energy generation technologies (with its typical production profiles). Thereby the flows of energy within ICES are studied and evaluated for different technology mixes, different community preferences and different community compositions. The goal of this research is to determine to what extent ICES can contribute to energy autonomy and at the same time, to the reduction of CO2 emission. Therefore a multi-objective optimisation of ICES is performed, in which the different available technologies, community preferences and community compositions are considered. The three optimisation preferences are: energy costs minimisation, CO2 emission reduction and energy autonomy maximisation. The main focus lies on the impact this optimisation has on households. A literature study and a stakeholder analysis are performed to identify the most suitable technologies to implement. A state-of-the-art ICES model is designed in MATLAB, to provide founded outcomes and to underpin the answers to the research questions. With this tool, the optimal technology mix is determined, based on different community specific parameters, valued on their technical, economic and environmental impact. Model input parameters include: demand profiles, weather data, and production profiles. The ICES model consists of two main parts. First a model on household level is designed, which is used to select the optimal technology mix for each type of household and for each optimisation preference. Different technologies are implemented to fulfil energy needs. Energy exchange with the electricity grid allows households to trade excess energy. The four types of households that are considered are: one adult household, two adult household, family household and pensioner household. Three performance indicators are mapped to quantify the performance of the technology mix at household level: energy price, CO2 emission and energy autonomy. The optimisation process at household level results in twelve optimal technology mixes. These twelve sub-results are used in the ICES model. Results of the household level model are stored and loaded in the ICES level model. With the ICES level model, the selected households with their optimal technology mix are combined to form an energy community. Stored household parameters are initialised (e.g. performance indicators, residual energy demand and excess energy profiles). An algorithm is developed, to distribute any excess energy among households that could not fulfil their own demand. Community ideology has a central role during the distribution of energy within ICES. Energy is imported from other community members at average levelised costs of energy production (LCOE). At each particular hour of the day, all households that import energy from ICES pay the same price per kWh for this energy. Households that export energy to ICES receive their full LCOE. Thereby they cover their investment costs, but are not stimulated to over-invest or over-produce. Energy is allocated to the demanding households in ratio to their demand. Energy exchanges within ICES is encouraged by this pricing mechanism. Energy that is being exported within ICES, is allocated to the exporting households in proportion of their total production in relation to the total ICES production. Exporting revenues and benefits from importing, thereby are equally distributed amongst contributing households. Energy that is not being used within ICES is exported to the grid at APX price. Demand that cannot be fulfilled within ICES is being supplied by the grid, at retail price. The outcome of the household level optimisation shows, for a purely financial optimisation preference (over a lifetime of 20 years), a 10% energy costs reduction is possible. This also results in a 25% CO2 reduction. With the use of ICES, energy costs and CO2 emission both reduce by another 10%. There are slight variations between results from different community compositions (less than 5% variation). The initial optimisation preference is of higher influence than the community composition. CO2 emission optimisation shows a larger CO2 emission reduction is possible, however energy costs increase quickly when opting for a large reduction of carbon emission. A 50% CO2 reduction is conceivable at ICES level, however this will increase the energy costs by 60%. In the case of CO2 reduction maximisation, almost all reduction is ascribed to the implemented technologies and ICES has little impact. This is also due to the fact that a low carbon intensive configuration exploits as much distributed generation as possible, implying a high energy autonomy at household level. Energy autonomy optimization is expensive and inefficient at household level. The technology mix that is able to supply peak demand, is largely over dimensioned during low demand hours. This is expensive, since investment is made for the full capacity. It is inefficient because technologies operate most of the time far below their optimal operating point. Also low annual energy production in comparison with the installed capacity causes high LCOE. Demand peaks will result in a technology mix that is able to supply the occasional occurring demand peaks, but essentially for the largest part of the time is over dimensioned. Without ICES, the export of excess energy is less profitable. Of all performance indicators, the largest contribution of ICES is observed in energy autonomy. Besides the different community compositions and technology mixes, a selection of additional scenarios is analysed. The different scenarios that are studied are electric vehicle (EV) penetration, stationary storage penetration, carbon pricing, scale effect and non-energy-producing household implementation. The electricity exchange price exponentially increases at high EV penetration level. A low EV penetration level has no negative effect, as long as there is sufficient available excess energy within ICES. The first EV owners and the last households without EV will benefit the most from ICES. The time mismatch between EV charging hours and renewable peak production asks for a solution, such as load shifting or temporary storage of renewable energy that is not used at time of production. The effect of stationary storage at household level, strongly relates with the effect ICES has on the performance indicators at household level. A high penetration ratio of stationary storage increases households’ individual performance, but this also means the contribution of ICES becomes less. When batteries are installed at all households, the total energy exchange within ICES reduces with 85%. Due to the (still) high capital costs of batteries, the total energy costs are lower when using ICES instead of batteries, while the overall performance is comparable. The financial effect of carbon pricing is relatively small, compared to the total annual energy costs and is calculated to be 150 on annual base at most. The effect of scale shows that an increased number of households slightly increases the carbon reduction and energy autonomy. Adding non-energy-producing households is possible without noticeably reducing performance indicators, up to a level of 20%. This research shows ICES has potential to reduce carbon emission (with maximum 50%), increase energy autonomy (up to 100%) and reduce energy costs (with maximum 20%). The multi-objective optimum is found at 20% CO2 emission reduction, 95% energy autonomy and 20% energy costs reduction. This shows ICES can be a promising solution in the trajectory towards a more efficient and low-carbon energy system. When the observed barriers are reduced and the right technology mix is used, ICES offers a valuable contribution to the reduction of CO2 emission at affordable costs. It offers perspective to an energy system that emphasizes on community engagement and equity for its community members.