The planning of heating and cooling supply and demand is key to reaching climate and sustainability targets. At the same time, data for planning are scarce for many places in Europe. In this study, we developed an open source dataset of gross floor area and energy demand for space heating and hot water in residential and tertiary buildings at the hectare level for EU28 + Norway, Iceland, and Switzerland. This methodology is based on a top-down approach, starting from a consistent dataset at the country level (NUTS 0), breaking this down to the NUTS 3 level and further to the hectare level by means of a series of regional indicators. We compare this dataset with data from other sources for 20 places in Europe. This process shows that the data for some places fit well, while for others, large differences up to 45% occur. The discussion of these results shows that the other data sources used for this comparison are also subject to considerable uncertainties. A comparison of the developed data with maps based on municipal building stock data for three cities shows that the developed dataset systematically overestimates the gross floor area and heat demand in low density areas and vice versa. We conclude that these data are useful for strategic purposes on aggregated level of larger regions and municipalities. It is especially valuable in locations where no detailed data is available. For detailed planning of heating and cooling infrastructure, local data should be used instead. We believe our work contributes towards a transparent, open source dataset for heating and cooling planning that can be regularly updated and is easily accessible and usable for further research and planning activities.
The optimal placement of Distributed Generation (DG) has attracted many researchers' attention recently due to its ability to obviate defects caused by improper installation of DG units, such as rise in system losses, decline in power quality, voltage increase at the end of feeders and etc. This paper presents a new advanced method for optimal allocation of DG in distribution systems. In this study, the optimum location of DG units is specified by introducing the power losses and voltage profile as variables into the objective function. Particle Swarm Optimization (PSO) and Clonal Selection Algorithm (CLONALG) are two methods which have been applied to optimize different objective functions in previous studies. In this paper, the Combination of Particle Swarm Optimization and Clonal Selection Algorithm (PCLONALG) is utilized as a solving tool to acquire superior solutions. Considering the fitness values sensitivity in PCLONALG process, it is necessary to apply load flow for decision making. Finally, the feasibility of the proposed technique is demonstrated for a typical distribution network and is compared with the PSO and CLONALG methods. The experimental results illustrate that the PCLONALG method has a higher ability in comparison with PSO and CLONALG, in terms of quality of solutions and number of iterations. The approach method has the preferences of both previous methods. Via immunity operation, the diversity of the antibodies is maintained and; the speed of convergence is ameliorated by operating particle swarm intelligence.
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