Shaping the future energy markets with hybrid multimicrogrids by sequential least squares programming

This paper presents a techno-economic model of two interconnected hybrid microgrids (MGs) whose electricity and thermal dispatch strategy are managed with Sequential Least Squares Programming (SLSQP) optimization technique. MGs combine multiple thermal and electric power generation, transmission, and distribution systems as a whole, to gain a tight integration of weather-dependent distributed renewable generators with multiple stochastic load profiles. Moreover MGs allow to achieve an improvement in the return of investment and better cost of energy. The first part of the work deals with a method to obtain an accurate prediction of climate variables. This method makes use of Fast Fourier Transform (FFT) and polynomial regression to manipulate climate datasets issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). The second part of the work is focused on the optimization of interconnected MGs operations through the SLSQP algorithm. The objective is to obtain the best financial performance (IRR) when clean distributed energy resources (DERs) are exchanging both thermal and electric energy. SLSQP optimizes the energy flows by balancing their contribution with their nominal Levelized Cost of Energy (LCOE). The proposed algorithm is used to simulate innovative business scenarios where revenue streams are generated via sales of energy to end users, sell backs and deliveries of demand response services to the other grids. A business case dealing with two MGs providing clean thermal and electric energies to household communities nearby the city of Bremen (Germany) is examined in the last part of the work. This business case with a payback in two years, an internal rate of return (IRR) at 65% and a LCOE at 0.14 €/kWh, demonstrates how the interconnection of multiple hybrid MGs with SLSQP optimization techniques, makes renewable and DERs outcompeting and could strand investments in fossil fuel generation, shaping the future of clean energy markets

Matrix Ordering Strategies for Process Engineering: Graph Partitioning Algorithms for . . .

The solution of large-scale chemical process simulation and optimization problems using parallel computation requires algorithms that can take advantage of multiprocessing when solving the large, sparse matrices that arise. Parallel algorithms require that the matrices be partitioned in order to distribute computational work across processors. One way to accomplish this is to reorder the matrix into a bordered block-diagonal form. Since this structure is not always obtained from the equation generation routine, an algorithm to reorder the rows and columns of the coecient matrix is needed. We describe here a simple graph partitioning algorithm that creates a bordered block-diagonal form that is suitable for use with parallel algorithms for the solution of the highly asymmetric sparse matrices arising in process engineering applications. The method aims to create a number of similarly sized diagonal blocks while keeping the size of the interface matrix, which may represent a bottleneck in the parallel computation, reasonably small. Results on a wide range of test problems indicate that the reordering algorithm is able to nd such a structure in most cases, and requires much less reordering time than previously used graph partitioning methods