Hybrid AC/DC Provisional Microgrid Planning Model Considering Converter Aging

Building integrated photovoltaics is one of the key technologies when it comes to electricity generation in buildings, districts or urban areas. However, the potential of building façades for the BIPV system, especially in urban areas, is often neglected. Façade-mounted building integrated photovoltaics could contribute to supply the energy demand of buildings in dense urban areas with economic feasibility where the availability of suitable rooftop areas is low. This paper deals with the levelised cost of electricity (LCOE) of building integrated photovoltaic systems (BIPV) in the capitals of all the European member state countries plus Norway and Switzerland and presents a metric to investigate a proper subsidy or incentive for BIPV systems. The results showed that the average LCOE of the BIPV system as a building envelope material for the entire outer skin of buildings in Europe is equal to 0.09 Euro per kWh if its role as the power generator is considered in the economic calculations. This value will be 0.15 Euro per kWh if the cost corresponding to its double function in the building is taken into the economic analysis (while the average electricity price is 0.18 Euro per kWh). The results indicate that the BIPV generation cost in most case studies has already reached grid parity. Furthermore, the analysis reveals that on average in Europe, the BIPV system does not need a feed-in tariff if the selling price to the grid is equal to the purchasing price from the grid. Various incentive plans based on the buying/selling price of electricity from/to the main grid together with LCOE of the BIPV systems is also investigated. View Full-TextpublishedVersio

Bi-Level Decomposition Approach for Coordinated Planning of an Energy Hub With Gas-Electricity Integrated Systems

Integrationof multiple energy systemsand the presence of smart energy hubs have provided increased flexibility and improved efficiency for the system. In this article, a bi-level decomposition approach (BLDA) is presented for coplanning of electricity and gas networks as well as the energy hub in distribution networks. The proposed multistage planning determines the investment candidates with optimum capacity for the components of integrated systems. Due to the complexity and nonlinearity of the models and energy subsystems interactions, the expansion planning problem is a difficult task with many limitations, especially for large-scale systems. To overcome these obstacles, achieve an optimum response and reduce computation time, a mixed integer linear programming model and a new BLDA methodology are developed in this article. Moreover, to evaluate the effectiveness and superiority of the proposed approach, the interactions among the energy systems are simulated in a large-scale distribution system and the results are compared.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Artificial neural networks for resources optimization in energetic environment

Resource Planning Optimization (RPO) is a common task that many companies need to face to get several benefits, like budget improvements and run-time analyses. However, even if it is often solved by using several software products and tools, the great success and validity of the Artificial Intelligence-based approaches, in many research fields, represent a huge opportunity to explore alternative solutions for solving optimization problems. To this purpose, the following paper aims to investigate the use of multiple Artificial Neural Networks (ANNs) for solving a RPO problem related to the scheduling of different Combined Heat & Power (CHP) generators. The experimental results, carried out by using data extracted by considering a real Microgrid system, have confirmed the effectiveness of the proposed approach

Maximizing the economic benefits of a grid-tied microgrid using solar-wind complementarity

The increasing use of intermittent, renewable energy sources (RESs) for electricity generation in microgrids (MGs) requires efficient strategies for reliable and economic operation. Complementarity between RESs provides good prospects for integrating several local energy sources and reducing the costs of MG setup and operations. This paper presents a framework for maximizing the economic benefits of a grid-tied MG by exploiting the spatial and temporal complementarity between solar and wind energies (solar-wind complementarity). The proposed framework considers the cost of energy production from different RESs and the cost of bi-directional energy exchange with the main grid. For a given RES mix, a minimum system power loss (SPL) threshold can also be determined. However, at this SPL threshold, MG energy exchange cost is not always minimized. The framework determines the optimized SPL value (above the threshold) at which MG energy exchange cost gets minimized. Through this framework, MG operator can decide appropriate RES mix and can achieve various tradeoffs according to the energy production cost, solar-wind complementarity of the site and its required economic objectives

A Data-Driven Two-Stage Distributionally Robust Planning Tool for Sustainable Microgrids

This paper presents a data-driven two-stage distributionally robust planning tool for sustainable microgrids under the uncertainty of load and power generation of renewable energy sources (RES) during the planning horizon. In the proposed two-stage planning tool, the first-stage investment variables are considered as here-and-now decisions and the second-stage operation variables are considered as wait-and-see decisions. In practice, it is hard to obtain the true probability distribution of the uncertain parameters. Therefore, a Wasserstein metric-based ambiguity set is presented in this paper to characterize the uncertainty of load and power generation of RES without any presumption on their true probability distributions. In the proposed data-driven ambiguity set, the empirical distributions of historical load and power generation of RES are considered as the center of the Wasserstein ball. Since the proposed distributionally robust planning tool is intractable and it cannot be solved directly, duality theory is used to come up with a tractable mixed-integer linear (MILP) counterpart. The proposed model is tested on a 33-bus distribution network and its effectiveness is showcased under different conditions

Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm

Microgrids (MGs) are capable of playing an important role in the future of intelligent energy systems. This can be achieved by allowing the effective and seamless integration of distributed energy resources (DERs) loads, besides energy-storage systems (ESS) in the local area, so they are gaining attraction worldwide. In this regard, a DC MG is an economical, flexible, and dependable solution requiring a trustworthy control structure such as a hierarchical control strategy to be appropriately coordinated and used to electrify remote areas. Two control layers are involved in the hierarchy control strategy, including local- and global-control levels. However, this research focuses mainly on the issues of DC MG’s local control layer under various load interruptions and power-production fluctuations, including inaccurate power-sharing among sources and unregulated DC-bus voltage of the microgrid, along with a high ripple of battery current. Therefore, this work suggests developing local control levels for the DC MG based on the hybrid particle swarm optimization/grey wolf optimizer (HPSO–GWO) algorithm to address these problems. The key results of the simulation studies reveal that the proposed control scheme has achieved significant improvement in terms of voltage adjustment and power distribution between photovoltaic (PV) and battery technologies accompanied by a supercapacitor, in comparison to the existing control scheme. Moreover, the settling time and overshoot/undershoot are minimized despite the tremendous load and generation variations, which proves the proposed method’s efficiency