1,721,287 research outputs found
Optimal energy storage system positioning and sizing with robust optimization
Full text linkEnergy storage systems can improve the uncertainty and variability related to renewable energy sources such as wind and solar create in power systems. Aside from applications such as frequency regulation, time-based arbitrage, or the provision of the reserve, where the placement of storage devices is not particularly significant, distributed storage could also be used to improve congestions in the distribution networks. In such cases, the optimal placement of this distributed storage is vital for making a cost-effective investment. Furthermore, the now reached massive spread of distributed renewable energy resources in distribution systems, intrinsically uncertain and non-programmable, together with the new trends in the electric demand, often unpredictable, require a paradigm change in grid planning for properly lead with the uncertainty sources and the distribution system operators (DSO) should learn to support such change. This paper considers the DSO perspective by proposing a methodology for energy storage placement in the distribution networks in which robust optimization accommodates system uncertainty. The proposed method calls for the use of a multi-period convex AC-optimal power flow (AC-OPF), ensuring a reliable planning solution. Wind, photovoltaic (PV), and load uncertainties are modeled as symmetric and bounded variables with the flexibility to modulate the robustness of the model. A case study based on real distribution network information allows the illustration and discussion of the properties of the model. An important observation is that the method enables the system operator to integrate energy storage devices by fine-tuning the level of robustness it willing to consider, and that is incremental with the level of protection. However, the algorithm grows more complex as the system robustness increases and, thus, it requires higher computational effort
Economic benefits redistribution methodology for renewable energy communities
No full textA large number of renewable energy communities are expected to be created in the near future, and, also in view of the funding that has been allocated in the various national climate and energy plans, there will be interest in their development from entities with different needs belonging to the institutional, industrial, commercial, and tertiary sectors. Given the possibility that these entities will be participated in by a variety of parties (individuals, SMEs, local governments, etc.) and in view of the particular "ethical nature" of RECs, it is to be expected that the remuneration of members will be in accordance with a principle of equity and in proportion to the ability to generate income for the community itself. For these reasons, a categorization of members according to their characteristics as renewable energy producers, prosumers or mere consumers is necessary. In this context, this article, with reference to an example case related to current legislation in Italy, proposes a model for the distribution of benefits related to energy production and sharing within the renewable energy community. These benefits concern those of the energy produced and simultaneously shared/absorbed by the members of the energy community as well as those related to the valorization of the energy fed into the grid, in excess of the consumption of the users of the energy community itself. The results obtained by applying the proposed methodology for the redistribution of the economic benefits accrued by the community among its participants show the usefulness of this distribution model in quantitative and qualitative terms, according to principles of equity and proportionality
IMPACT OF ELECTRIC VEHICLE CHARGING ON ITALIAN LV DISTRIBUTION NETWORK
No full textThe transport sector (road, rail, shipping, and aviation) is responsible for 37% of global CO2 emissions. Passenger cars and light commercial vehicles are respectively responsible for around 12% and 2.5% of total European CO2 emissions.
In order to reduce the impact of the transport sector, strategic actions (e.g., electrification of road vehicles, operational and technical energy efficiency measures, and the adoption of policies to encourage a modal shift to lower carbon-intensive travel options) have to be taken. However, the wide adoption of electric cars could impact the distribution network due to the spread of charging infrastructures. Indeed, the LV networks will host charging infrastructures mostly at residential premises, and uncontrolled charging (e.g., all the vehicles charge at the same time once they are back home) could lead to a violation of the technical limits of the network.
Solutions to avoid such drawbacks must be considered. Traditional planning approaches would ask for network reinforcement, while the modern one suggests the use of smart charging strategies to avoid simultaneous charging. In the paper, the two approaches will be compared through a set of representative Italian LV distribution feeders, and a general rule to be used by those DSOs that have to tackle this issue will be identified
Models for characterising the final electricity demand
Full text linkNowadays the consumption and generation profile estimation is of the greatest importance. New loads characterized by coincident peak of consumption (e.g., home charging of electric vehicles) or by high absorption peaks (heat pumps) are increasingly frequent. The presence of such loads must be carefully considered for network investments and for the optimization of asset management. Moreover, the massive diffusion of non-programmable renewable sources gives a leading role to the flexibility of demand, which is crucial for the success of the energy transition. The variety and difference of the electrical behaviour of LV customers, even nominally homogeneous, need stochastic methods for estimating the load profile on the LV/MV interfaces for the planning and the operation of distribution network, and for estimating the flexibility potential of demand. In this paper different techniques for modelling the demand composition are compared to evaluate the quality of the DSO models on real customers. In particular, the power peak of a given network section is calculated as key indicator for estimating the risk of overloading of lines and secondary substation transformers. Different methods of calculation have been applied on a dataset gathered with a recent measurement campaign in Italy by considering real LV distribution networks
Development of innovative systems for operation and control of electric power distribution networks: Management and optimal use of distributed generation and of nenewable energy resources
No full textThe integration of distributed energy resources for the production of electrical energy is related to the evolution of future power distribution networks in order to achieve: flexibility (meeting final user's requirements); accessibility (allowing access to local generation and particularly to renewables); reliability (ensuring the highest possible security and power quality levels); economy (allowing an adequate management of energy in an efficient and competitive way).In this framework, the paper presents some results of a joint research project involving five Italian universities. The focus is on the proposition and development of the main functionalities implemented into a centralized Distribution Management System (DMS) for the operation and control of the energy resources connected to distribution networks (generation, storage and loads) and of the network itself (automation systems, protection, etc.). Architecture, hierarchical structure and relevant functions for the control, operation and management of the active distribution network, are illustrated. © 2012 IEEE
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