Understanding the value of net metering outcomes for different averaging time steps

The installation of distributed energy resources (DER) heavily impacts on the power patterns of the prosumers. In fact, the variability of the generation, together with the technical characteristics of the storage systems, may introduce a huge variety in the shape of the net power curves seen from the point of common coupling (PCC). This leads to completely rethink the definition of the time series required to create homogeneous group of prosumers, for providing useful tools to manage the emerging paradigms in the electricity system, such as energy communities and local energy markets. Moreover, the differences between the local energy production and consumption at the PCC could become hidden, if the local energy management has to be considered as a private decision of the local user. In this case, only net metering (that implies a unique measurement of the net electricity taken from the grid) will be used to evaluate the impact on the network of the net power curves. Hence, new approaches are required to properly measure the electricity exchange at the PCC. This paper addresses how the net metering outcomes depend on the time resolution of the measured data, and how the information taken from net metering can be valued by giving different price rates to positive and negative values. Specific examples are provided to remark the importance of the time resolution to properly characterise the prosumers

Heuristic optimization of electrical energy systems: Refined metrics to compare the solutions

Many optimization problems admit a number of local optima, among which there is the global optimum. For these problems, various heuristic optimization methods have been proposed. Comparing the results of these solvers requires the definition of suitable metrics. In the electrical energy systems literature, simple metrics such as best value obtained, the mean value, the median or the standard deviation of the solutions are still used. However, the comparisons carried out with these metrics are rather weak, and on these bases a somehow uncontrolled proliferation of heuristic solvers is taking place. This paper addresses the overall issue of understanding the reasons of this proliferation, showing a conceptual scheme that indicates how the assessment of the best solver may result in the unlimited formulation of new solvers. Moreover, this paper shows how the use of more refined metrics defined to compare the optimization result, associated with the definition of appropriate benchmarks, may make the comparisons among the solvers more robust. The proposed metrics are based on the concept of first-order stochastic dominance and are defined for the cases in which: (i) the globally optimal solution can be found (for testing purposes); and (ii) the number of possible solutions is so large that practically it cannot be guaranteed that the global optimum has been found. Illustrative examples are provided for a typical problem in the electrical energy systems area – distribution network reconfiguration. The conceptual results obtained are generally valid to compare the results of other optimization problem

Losses Allocated to the Nodes of a Radial Distribution System with Distributed Energy Resources-A Simple and Effective Indicator

This paper presents the effectiveness of exploiting the losses allocated to the nodes of a radial distribution system as an indicator of the impact of the diffusion of distributed energy resources in the network. The calculation of the losses allocated to the nodes is not included in the commercial power flow solvers, even though the implementation of this calculation is simple and the results provide meaningful information. The interpretation of the allocated losses is illustrated in this paper, on the basis of the results obtained on a typical test network under different case studies

Impact of the time resolution for data gathering on loss calculation and demand side flexibility

Accurate data metering is needed for enabling demand side flexibility and the related services. Sufficient resolution in time of the data gathered is essential to obtain detailed information on how consumers and prosumers use electricity. This paper addresses two specific points concerning the effects of the time resolution on (i) the estimation of the network losses, and (ii) the assessment of the average power peak magnitude and duration. Specific indicators are introduced to estimate the losses and assess the peak power based on the load pattern shape. These effects are analysed based on examples taken from real measurements. The results clearly show that the time resolutions used today (from 15 min to 1 hour) are insufficient to perform effective assessments oriented to enhance demand side flexibility. Interval metering with better resolutions (1 min or less) or innovative technologies such as event-driven energy metering should be used to provide significantly better solutions

Economical comparison of CHP systems for industrial user with large steam demand

In this paper cogeneration benefits applied to a user with a high steam demand are analyzed. The methodology for the feasibility study and the economical analysis of the investment is presented under the Italian legislative framework. The methodology is applied to an actual case and a detailed description and discussion of all data input is provided. Especially this last key point will be faced using starting data usually available in these kind of studies (i.e., not very detailed for thermal consumption). Finally a comparison of different CHP technologies and a sensitivity analysis is done

Evaluating car-sharing switching rates from traditional transport means through logit models and Random Forest classifiers

Positive impacts of car-sharing, such as reductions in car ownership, congestion, vehicle-miles-traveled and greenhouse gas emissions, have been extensively analyzed. However, these benefits are not fully effective if car-sharing subtracts travel demand from existing sustainable modes. This paper evaluates substitution rates of car-sharing against private cars and public transport using a Random Forest classifier and Binomial Logit model. The models were calibrated and validated using a stated-preference travel survey and applied to a revealed-preference survey, both administered to a representative sample of the population living in Turin (Italy). Results of the two models show that the predictive power of both models is comparable, albeit the Logit model tends to estimate predictions with a higher reliability and the Random Forest model produces higher positive switches towards car-sharing. However, results from both models suggest that the substitution rate of private cars is, on average, almost five times that of public transport

Bridging the Flexibility Concepts in the Buildings and Multi-energy Domains

paper aims to stimulate a discussion on how to create a bridge between the concept of flexibility used in power and energy systems and the flexibility that buildings can offer for providing services to the electrical system. The paper recalls the main concepts and approaches considered in the power systems and multi-energy systems, and summarises some aspects of flexibility in buildings. The overview shows that there is room to strengthen the contacts among the scientists operating in these fields. The common aim is to identify the complementary aspects and provide inputs to enhance the methodologies and models to enable and support an effective energy and ecologic transition

Optimisation of Generation Models for Clusters of Photovoltaic Plants

The present work analyses the actual production profiles of a group of tens of thousands of PV plants. Actual PV generation profiles are represented as hourly average powers gathered from the energy meters measured at the point of common coupling with the grid. After filtering, data cleaning and statistical analysis, reference PV plants are selected for the improvements of the PV generation models. Production profiles are calculated by using literature generation models, with weather data as inputs. An optimisation is performed on the parameters of the literature models to minimise the differences between the cumulative distribution functions of calculated profiles and measured data. The study compares the performance of the different models and shows how the optimisation increases the quality of the calculated profiles

Application of artificial dynamics to represent non-isolated single-input multiple-output DC-DC converters with averaged models

This paper presents for the first time the application of a method based on the transformation of the differential algebraic equations of non-isolated Single-Input Multiple Output (SIMO) DC-DC converters into a set of ordinary differential equations, by using artificial dynamics whose asymptotic convergence to the solution is guaranteed by the satisfaction of the relevant Lyapunov conditions. The mathematical formulation is simpler than in other formulations applied in the literature to study non-isolated SIMO DC-DC converters, and encompasses the use of sensitivity functions. The results show that the proposed solution represents in a fully accurate way the dynamics of the averaged models of Zeta Buck-Boost and Cúk Boost Combination converters

Statistical Validation and Power Modelling of Hourly Profiles for a Large-Scale Photovoltaic Plant Portfolio

In the last decades, the number of photovoltaic (PV) generators significantly increased over the world. An accurate analysis of the massive data gathered from the meters of the PV plants can improve the management of their intermittent generation. This paper presents a methodology to analyse the production profiles of a portfolio of thousands of PV plants installed in an Italian region. The procedure faces the problem of filtering poor and incomplete data, then uses a stratified sampling technique for the statistical validation of the remaining profiles. Finally, the checked production profiles are used to adjust the energy model to better match the measured data and calculate the whole PV portfolio production