Abstract The insertion of new distributed energy resources, such as distributed generation (DG), energy storage systems (ESS), demand response (DR), and microgrids (MG), is emerging, bringing new challenges to the current distribution network. In this regard, the active distribution networks (I) with multi-microgrids concept appears. The present paper proposes a hierarchical (master-slave problem) computational model to achieve optimal coordinated operation of multi-microgrids connected to an I. Day-ahead operation planning of an I was formulated as a multiperiod non-linear optimal power flow model, resulting in a non-linear optimization problem, additionally, the day-ahead operation planning of MGs was formulated as a multiperiod linearized optimal power flow resulting in a mixed-integer linear optimization problem. Numerical results on four different test-system microgrids connected to a 359-nodes ADNs test-system belonging to a Brazilian distribution company show the effectiveness of the proposed model and solution strategy. Three cases have been tested: with a maximum load-shedding restriction, without this restriction, and considering insertion of DG. Besides, the hierarchical model can evaluate how much losses and load shedding take effect without integrated operation and expansion planning of emerging distributed networks. This study showed the importance of analyzing the systemic impact of integrating multi-MGs and I synergistic operation interactions, resulting in improvements in the voltage quality levels, operation costs, and power losses. The results showed that, including DG in the system, the costs were reduced by 13,48% compared to the case base.

Abstract The optimization of microgrids present challenges such as managing distributed energy resources (DERs) and the high reliance on intermittent generation such as PV and wind turbines, which present an aleatory behavior. The most popular techniques to deal with the uncertainties are stochastic optimization, which comes with a high computational burden, and adaptive robust optimization (ARO), which is often criticized for the conservativeness of its solutions. In response to these drawbacks, this work proposes a mixed-integer linear programming (MILP) model using a data-driven robust optimization approach (DDRO) solved by a two-stage decomposition using the column-and-constraint generation algorithm (C&CG). The DDRO model uses historic data to create the bounds of its uncertainty set, eliminating the conservativeness created by the arbitrary definition of the uncertainty set that is seen in ARO while maintaining a low computational burden. The DDRO model applied was not previously utilized in MGs, only in bulk power systems. A benchmark MG system was simulated for a 24-hour period without uncertainties, with uncertainties using ARO (15% uncertainty budget) and with uncertainties and DDRO. The operational costs without uncertainty were $124,600,60, while the ARO approach rose those costs by 32.5% ($ 165,137.18). Finally, the DDRO approach managed to keep the costs in $ 126,934.54, a mere 1.8% increase from the base case without uncertainty. All simulations were performed in less than 1 minute. The results confirm a) the advantages of bounding the uncertainty set with historical data instead of an arbitrary definition of bounds and b) the fast-converging times of DDRO.

Abstract Electricity is undoubtedly one of the most important resources in the modern world. As the demand for electric energy increases, conventional resources that are transformed into electric energy are being exhausted, generating a need to search for alternative sources, resulting in a significant increase in energy costs. This study presents an integrated project of an intelligent microgrid and energy management aimed at reducing energy costs. At the Federal University of Paraná (UFPR), electricity represents an annual cost of over BRL 13 million, which is the third largest operating expense of the university. In addition, the public education budget in Brazil has been decreased in recent years. Therefore, this study was conducted within the scope of UFPR and aimed to analyze three alternatives to reduce electric energy costs: i) demand management through an analysis of energy bills, ii) migration to the free energy market, and iii) the development of an in-house photovoltaic generation facility. A computational tool to optimize the contracted demand and simulate the annual savings with the free market and distributed generation projects was developed using Microsoft Excel. Payback, the net present value, and the internal rate of return were calculated. Finally, the economic viability of all alternatives was proven, with demand management demonstrating an economic potential of greater than BRL 500,000 per year, use of the free market saving more than BRL 300,000, and the developed in-house photovoltaic generation system achieving an economic impact of more than BRL 600,000 per year.

Abstract The demand for electricity is growing worldwide. At the same time, the non-renewable natural resources that account for a large proportion of the global energy matrix are rapidly depleting, which will pose a major challenge in the near future. Therefore, micro-grid models that use renewable energy sources, such as solar and wind, are rapidly developing and are becoming economically viable alternatives. The objective of this study was to evaluate the economic viability of installing solar and wind power generation systems in the NOVVALIGHT electrical components factory located in Campo Largo, Paraná, Brazil. The most viable model was the combination of solar and wind energy, which would generate approximately 260 MWh of energy per year. Using financing provided by the Brazilian Bank for Economic and Social Development (BNDES), this proposal has an eight-year payback period, net present value of BRL 149,097.42, and internal rate of return of 18%, demonstrating its economic viability.