2 State of the art
2.1 Solar maps
The imperative drive toward energy transition has driven research into harnessing renewable energy sources. Among these, solar power stands out due to its vast potential. Onsite photovoltaic (PV) micro-generation is emerging as a compelling solution, as it not only promotes sustainability but also reduces electricity distribution and transmission costs and losses [11]. In this context, rooftops emerge as optimal locations on buildings for solar energy harvesting and electricity generation.
Assessing rooftop solar potential is pivotal for effective PV system integration. One crucial factor is the availability of usable roof area, which takes into account shading, obstructions, and architectural characteristics. Consequently, developing methods to accurately identify suitable roof surfaces for PV installation in urban environments has become a research focus.
Several techniques have been devised for this purpose, categorised into three primary methods: constant-value methods, manual selection methods, and geographic information system (GIS)-based methods. Constant-value methods assume a fixed portion of the total roof area as suitable for PV panels, considering factors like shading from neighboring structures [12]. Manual selection methods, on the other hand, rely on sources like aerial imagery and platforms like Google Earth to evaluate the appropriateness of individual roof planes for PV installation [13].
Among these methods, GIS-based approaches are particularly noteworthy. Using advanced geographic information systems and technologies, these methods provide a more precise evaluation of rooftop solar potential compared to constant-value methods[11]. These techniques incorporate satellite image analysis [14], physical models [15], image processing [16], and even machine learning algorithms [17]. Some papers even use multiple of the previously mentioned methods such as image processing and ML [walch?]. Their precision and scalability make them suitable for analyzing extensive datasets, allowing urban planners and energy stakeholders to make informed decisions regarding rooftop solar integration.
2.2 Grid connected PV systems, mobility and heating electrification
In recent years, the integration of photovoltaic (PV) systems into power grids has gained significant attention due to its potential to provide clean and sustainable energy. In Geneva, Switzerland, this trend is expected to intensify in the near future. The Canton of Geneva has set ambitious targets for increasing the adoption of PV systems, with a focus on residential, commercial, and industrial sectors [18]. With supportive regulations and incentives, the installation of PV systems is projected to grow rapidly, aiming to reach a significant share of the energy mix by 2030 [19]. Government initiatives, including subsidies and favourable net metering policies, are pivotal in encouraging PV adoption [20, swiss-net-metering]. These policies allow energy generated by PV systems to be fed back into the grid, enabling consumers to offset their consumption costs.
A typical grid connected PV system mainly consists of PV panels, a DC/DC converter, DC link, DC/AC inverter, filter and control blocks. The controllers on the PV side (“DC side controller”) and the grid side (“AC side controller”), perform a variety of operations while supplying active power to the utility grid process [21]. The DC PV side controller maximises solar PV power under uncertain environmental conditions by employing a Maximum power point tracking (MPPT) algorithm and providing the DC voltage reference value to the DC voltage controller on the AC side. The AC side controller inverts the DC production to AC in order to allow it’s use for the local load or it’s integration into the grid. Furthermore, the inverter is responsible to meet the electricity grid standards and requirements when exporting electricity. Figure 2.1 illustrates the main components of a grid connected PV system.
Figure 2.1: Main components in a grid-connected PV-system without battery reserves.
Source: [21]
The widespread installation of PV systems, while contributing to renewable energy generation, poses challenges to the existing electric grid infrastructure. Numerous scientific studies have delved into the impacts of grid-connected PV systems on the electricity grids [23], and particularly on the LV grid [24]. Researchers have pointed out potential issues, such as voltage rise, frequency instability, and the potential overloading of transformers and distribution lines[26]. The intermittent nature of solar power generation adds to the complexity, requiring the grid to manage variable input.
Simultaneously, the electrification of the heating and mobility sectors adds a new layer of complexity to grid management. As the installation of PV systems escalates, the growth of electric vehicles (EVs) is projected to follow suit. EV technologies can be segmented into two main components, such as electric propulsion system and EV charging system [25], as shown in 2.2. The electric propulsion system in an EV system provides the required energy for the EV motor while driving. On the other hand, the EV charging system provides energy for the EV battery while parked and connected to the grid.
Figure 2.2: Electric propulsion system and EV charging system.
Source: [25]
The charging system component of the EVs, hold potential for grid support by acting as energy sinks during times of high production and sources during peak demand [25]. Several studies have investigated the concept of Vehicle-to-Home (V2H) systems, which facilitate energy flow from EVs to the grid, thus potentially alleviating some of the adverse effects of PV systems on grid stability [27].
However, despite these promising potentials, challenges remain. The nascent state of V2H and energy storage technologies, along with high initial costs, inhibits widespread adoption. These technologies demand further development and cost reduction to become commercialised options [29]. For DSOs it is however important to know how will the grid react if this developments don’t arrive in time. Only few studies are however available to show the impacts of mobility electrification [30] if these technologies are not ready to be commercialised in time.
In the process of heating sector electrification, heat pumps are progressively assuming a more significant role. Electrically-driven heat pumps utilize electricity to elevate low exergetic heat to higher temperatures, thereby raising the exergy level through a vapor compression cycle. The heat extraction is sourced from elements like ambient air, water, or the ground. Heat pumps have gained recognition as a low \(CO_2\) emission technology for residential heat generation. The heat pump coefficient of performance (COP) and the emissions associated with electricity generation play a pivotal role in determining the emission levels during the heat pump’s operational phase [31]. The effects of electrifying heating systems on the power grid have also been a subject of examination. An investigation centred on the UK grid revealed that decarbonising heating would inevitably necessitate extensive network reinforcement [32]. Another inquiry, conducted on the Danish grid, explored the operational flexibility of Electric Vehicles (EVs) and Heat Pumps (HPs) when integrated into a Low Voltage (LV) grid. In this context, a particular penetration level of these technologies was simulated as the benchmark scenario, using meticulous load profiles and a steady-state time series analysis.
Interestingly, few studies concurrently examine PV generation, heating electrification, and mobility electrification on the LV grid. The tendency to analyse these elements separately can obscure the effects of their combined integration on grid stability and the requisite reinforcements. Such compartmentalisation might result in an incomplete grasp of the genuine challenges arising from the integration of PV systems and electric vehicles.
Addressing the impending grid congestion resulting from the integration of PV systems, electric mobility and the electrification of heating involves two predominant methods: Firstly, a static simulation approach, which typically encompasses a single time step and often employs stochastic Monte Carlo simulation techniques [33]. Secondly, the majority of studies [35] adopt time series analyses with a multi-objective optimisation model. The annual time-series are predominantly favoured due to the volatile nature of renewable energy technologies and their reliance on weather conditions [35]. For on the field analysis, Distribution System Operators (DSOs) frequently rely on an analytical static load approach [36] for traditional power grid planning due to its simplicity and quick application. This approach calculates the aggregated peak load of multiple grid customers by multiplying the number of customers with the respective coincidence factor and the average individual peak load[36]. Although not fully representative of real-world behaviour, it approximates the worst-case scenario.
Moreover, recent research diverges concerning the grid regions under studying. While many studies overlook the precise grid region under investigation (e.g., [37]), some studies have explored urban [38] and rural [39] LV grids. However, these analyses often disregard the fact that different real-life housing types (such as single-family houses, multi-apartment residential buildings, etc.) correlate with distinct grid regions. Recognising these distinctions is important for assessing grid impacts, particularly in urban areas. Consequently, the existing body of research often treats the electric grid as a uniform entity, neglecting the diverse attributes characterising distinct neighbourhoods.