George B. Huitema

The calculation of the energy balance at the district level is complex since it includes a diverse set of loads, technologies, energy carriers, trading interactions between users and external grids (power, district heating/cooling, gas, etc.) and assumptions such as the identification of Primary Energy Factors (PEFs) in different contexts. This research validates the H2020 MAKING-CITY methodology for calculating the energy balance of Positive Energy Districts (PEDs) in two case studies: the cities of Groningen and Torrelago. For each case, the steps defined in the methodology are followed, dealing with assumptions on non-renewable Primary Energy Factors and critical elements regarding the district boundary. This research shows the applicability of the developed calculation methodology for cities in the design phase as well in the implementation phase of PEDs.

Positive energy districts (PEDs) are seen as a promising pathway to facilitating energy transition. PEDs are urban areas composed of different buildings and public spaces with local energy production, where the total annual energy balance must be positive. Urban areas consist of a mix of different buildings, such as households and service sector consumers (offices, restaurants, shops, cafes, supermarkets), which have a different annual energy demand and production, as well as a different consumption profile. This paper presents a data modeling approach to estimating the annual energy balance of different types of consumer categories in urban areas and proposes a methodology to extrapolate energy demands from specific building types to the aggregated level of an urban area and vice versa. By dividing an urban area into clusters of different consumer categories, depending on parameters such as surface area, building type and energy interventions, energy demands are estimated. The presented modeling approach is used to model and calculate the energy balance and CO2 emissions in two PED areas of the City of Groningen (The Netherlands) proposed in the Smart City H2020 MAKING CITY project.

Although it has been demonstrated that demand-side flexibility is possible, business application of residential and small tertiary demand response programs has been slow to develop. This paper presents a holistic demand response optimization framework that enables significant energy costs reduction for consumers. Moreover, buildings are introduced as main contributors to balance energy networks. The solution basis consists of a modular interoperability and data management framework that enables open standards-based communication along the demand response value chain. The solution is being validated in four large-scale pilot sites, which have diverse building types, energy systems and energy carriers. Furthermore, they offer diverse climatic conditions, and demographic and cultural characteristics to establish representative results.

In this paper, the application of demand-side flexibility to residential users and energy communities is discussed. From the H2020 project HOLISDER, it appears that selected Key Exploitable Results are not commercially interesting on their own as flexibility services but certainly are an add-on by cross-value stacking to regular energy services. For groups of prosumers, such as energy communities, cross-stacking can enlarge the awareness and commitment within the communities and, therefore, play an important role in the uptake of citizen communities as promoted by the EU Green Energy Package.

With the growth of renewable generated electricity in the energy mix, large energy storage and flexible demand, particularly aggregated demand response is becoming a front runner as a new participant in the wholesale energy markets. One of the biggest barriers for the integration of aggregator services into market participation is knowledge of the current and future flexible capacity. To calculate the available flexibility, the current aggregator pilot and simulation implementations use lower level measurements and device specifications. This type of implementation is not scalable due to computational constraints, as well as it could conflict with end user privacy rights. Black box machine learning approaches have been proven to accurately estimate the available capacity of a cluster of heating devices using only aggregated data. This study will investigate the accuracy of this approach when applied to a heterogeneous virtual power plant (VPP). Firstly, a sensitivity analysis of the machine learning model is performed when varying the underlying device makeup of the VPP. Further, the forecasted flexible capacity of a heterogeneous residential VPP was applied to a trade strategy, which maintains a day ahead schedule, as well as offers flexibility to the imbalance market. This performance is then compared when using the same strategy with no capacity forecasting, as well as perfect knowledge. It was shown that at most, the highest average error, regardless of the VPP makeup, was still less than 9%. Further, when applying the forecasted capacity to a trading strategy, 89% of the optimal performance can be met. This resulted in a reduction of monthly costs by approximately 20%.

Networks are becoming more dynamic, not only as a result of mobility, but also due to service requirements, business aspects, and other reasons. This dynamicity will also reflect on charging and billing, requiring a fresh look at charging and billing from both the business side and the technical side. This paper presents the concepts and mechanisms for dynamic charging and billing in ambient networks, and in general for next-generation business environments.