• Energy Research

Abstract Energy from the sun is weather-dependent. In modern electric grids that is a shortcoming; generation (and load) has to be regulated accordingly. This issue is a cornerstone for an effective transition to a renewable-based energy system. Weather forecast algorithms can predict photovoltaic production but, in real life conditions, their reliability is only partially effective with respect to the actual grid operation requirements. In the paper, Energy Storage Systems are adopted to compensate the mismatch between the injections of a photovoltaic power plant and the day-ahead market power schedule: the final goal is to achieve the full programmability of the photovoltaic resource by minimizing energy imbalances, as defined in the Italian regulatory framework, on an hourly basis. In particular, the optimal design of the storage apparatus (nominal power and capacity) is defined according to the regulating performances required. Moreover, three forecast models are tested to evaluate the impact of weather prediction accuracy on the ESS design. Finally, the benefit/cost ratio of the ESS application is assessed according to the main economic and technical parameters (ESS cost, round trip efficiency, lifespan). The analyses are performed on data measured on a real power plant, with hypotheses consistent with the actual Italian scenario.

In an effort to improve the stability and secure operation of the grid, regulatory bodies are opening Ancillary Services Markets participation to Distributed Energy Resources (DERs), energy storage systems, and demand response. Within this framework, this study proposes a model that simulates the coordinated operation of an aggregate of power plants, including non-dispatchable DERs and, as regulating units, Combined Heat and Power (CHP) generation and electrochemical energy storage systems. A Monte Carlo procedure is adopted to realistically create a population of aggregation scenarios. The real-time operation of the DER portfolio is managed through a Heuristic Greedy-Indexing logic, which allows the Aggregator to select the optimal control action to implement according to the technical and economic quantities characterizing the market and the grid. The techno-economic performance of the proposed algorithm is evaluated by simulating its interaction with the electricity markets. Finally, a sensitivity analysis is performed to analyze the profitability in different scenarios. The novel mathematical model proposed showed to be effective in managing a complex problem like the one at hand with an acceptable computational effort. The numerical results obtained confirmed that the aggregated participation in the market could provide interesting economic returns, especially if a CHP unit is involved as regulating unit, while the feasibility of the batteries adoption is still limited by the actual cost of the technology.

Distribution systems are subject to increasing penetration of dispersed generation. The energy injections of the generators impact on many technical issues, including energy losses occurring in the grid. Given the technical and economic importance of losses, several statistical approaches are proposed in the literature to evaluate the effects of dispersed generation on the energy lost in distribution networks. In principle, these approaches require a great number of load flow calculations. This paper provides a novel index aiming to avoid complex and computationally expensive statistical analysis for loss assessment. Such an index does not require any detailed characterisation of the energy flows over the network (load flow calculations). The performance of the index is tested using a model of a real medium voltage network in a wide set of generation scenarios. Each scenario is defined by a Monte Carlo algorithm that was developed ad hoc for this study. Moreover, advanced convergence criteria are provided to optimise the number of scenarios to process.

The appearance of new single-phase intensive and often coincident loads, such as the electric vehicle private charging stations can affect the operation of low voltage (LV) distribution networks. Unbalances among the three-phase loading are extremely common, even if the end-users of a given LV network have similar behaviors. Due to the coincidence of such loads, excessive voltage drops may often appear, altering the quality of supply voltage provided to the LV end-users. This work evaluates the effects of residential and other charging stations in parking lots of shopping malls or workplaces on the quality of supply voltage of LV grids. Then, a decentralized approach is proposed, based on the multi-agent system, which allows the different flexibility resources available on the grid to act together to improve the network quality. The agents cooperate to regulate the network nodal voltage, taking into account the characteristics of the resources they are controlling and the needs of their owners. A case study derived from a portion of the Italian distribution system demonstrates the validity of the approach in solving the network operation criticalities.

Shortages and poor diversification of generating capacity, high environmental impacts, and low qualities of supply and affordability are common characteristics of the electricity sector in most developing countries (DevCs). Recent cost reductions in the telecommunication sector and some generation technologies, especially renewable-based, with improved economic feasibility have paved the way for new electric infrastructures. This is especially true for emerging economies, as locally dispatched hybrid microgrids introduce flexibility and show the promise of technical and economic advantages. The design and integration of microgrids require detailed assessments to ensure the effective deployment of capital to minimize the risk of stranded assets and capital waste. Microgrids can be considered an affordable option for a rapid response to the electrification challenge, recognizing that, in the longer horizon, a sharing of resources with the bulk electrical power system will remain, technically and economically, advantageous. This article presents a comprehensive review of the approaches commonly adopted for microgrid electrification. A “real-life” study case is reported to highlight the operational challenges of a stand-alone microgrid versus a grid-connected system. The need for an improved regulatory framework is presented as the cornerstone problem to be solved to allow an effective integration of microgrids in the national grid.

In a liberalized energy market, policymakers cannot over-impose the deployment of new distributed generators, either in terms of location or in terms of size/technology; on the opposite, they are asked to promote incentives, penalties or constraints in order to foster a generation portfolio evolution fitting with the energy need of the loads. In the paper, given a local distribution grid, a two-step procedure is proposed to define the most effective energy policy, willing to drive a proper evolution of the generation portfolio, i.e., to maximize the renewable sources exploitation taking into account the grid constraints. The approach proposed is based on a stochastic (Monte Carlo) procedure. Given a generation portfolio, many scenarios are evaluated, changing generators’ nominal power, point of common coupling and also a slightly different technologies share. Actually, the final goal of the procedure proposed is to simulate the stochastic behavior of users with respect to the regional energy policy (i.e., to perform a multi-dimensional sensitivity analysis) in order to validate the proposed generation portfolio. In particular, in the first step of the procedure, it is defined a portfolio in which generators are aggregated with respect to the power plant technology (PV, wind, small hydro, big hydro, etc.). Such a portfolio is optimized in order to maximize the matching between local production and local consumption. In the second step, a Monte Carlo simulation is implemented to stochastically take into account a significant number of possible configurations of each portfolio (number of generators, unit size, location, etc.). Given the generator’s distribution, a probability index based on a Hosting Capacity concept is proposed as a performance index. Conductors’ thermal limits and slow voltage variations on the electrical network are evaluated for several generator’s distributions and for different dispersed generation penetrations. The final goal of the approach proposed is to define the optimal local generation portfolio fitting both with the load profiles and with the bounds of the distribution grid already in place. Such an output resulted to be a valuable piece of information for decisionmakers in order to properly promote regional energy planning policies. In order to validate the approach and demonstrate its capabilities, the procedure proposed has been applied to the real medium voltage distribution grid relevant to the Italian city of Aosta, i.e., real-life topologies, renewable-based generation and load fluctuation have been simulated.

This paper describes an advanced method of analysis and optimization of Medium Voltage (MV) distribution networks with the aim to increase the energy efficiency, based on the grid reconfiguration.

The goal of this work is to evaluate the impact of the e-mobility charging processes on the electric grid, in a real-life study case. An effective approach is proposed to study the increase in the energy consumption on the grid with respect to both grid operation and efficiency. The work is developed considering three different recharging technologies, slow (based on domestic users), fast (based on public charging stations), and very fast (based on enhanced public charging stations). Furthermore, three different technologies distributions are evaluated (e.g. different scenarios on charging station deployment are simulated). The results show that fast charging technologies could better fit with e.cars exploitment, but they could cause also a significant stress increase over the grid. The paper is devoted to quantify such effects.