Transition towards a full self-sufficiency through PV systems integration for sub-Saharan Africa: a technical approach for a smart blockchain-based mini-grid

Access to affordable, reliable and clean energy is an important sustainability goal of the United Nations. In areas where the public electricity grid is unreliable or unavailable, photovoltaic systems can be a solution. However, they are cost-intensive, mainly because of the energy storage systems. Mini-grids can be an answer for reducing upfront investment and overall system lifetime costs while increasing electricity availability. The mini-grid technology is mature, nevertheless, there are downsides when it comes to integrating existing solar systems of different manufacturers. The system topology is usually predefined and a central instance controls the mini-grid. Thus, the integration of existing power systems is difficult due to the communication constraints of these systems with the mini-grid controller. Including existing power systems into a decentralized mini-grid, can highly increase cost-efficiency. In a decentralized approach payments for the consumed energy between mini-grid actors are required. Accounting is, however, a complex administrative procedure, if the respective power systems are owned by different individuals and organizations. A transparent blockchain-based temper-proof approach can be a solution to automate metering and billing, allowing automatic payments between independent subsystem owners using smart contracts. In order to further optimize the smart mini-grid, an artificial intelligence learning algorithm for a dynamic electricity price needs to be developed. This smart and decentralized approach for building Mini-Grids is a novelty bringing solar systems one step closer to self-sufficiency. This paper describes how a smart mini-grid solution can be implemented using the Don Bosco Solar & Renewable Energy Center campus mini-grid in Tema, Ghana as a case study.

Smart Grid for Industry Using Multi-Agent Reinforcement Learning

The growing share of renewable power generation leads to increasingly fluctuating and generally rising electricity prices. This is a challenge for industrial companies. However, electricity expenses can be reduced by adapting the energy demand of production processes to the volatile prices on the markets. This approach depicts the new paradigm of energy flexibility to reduce electricity costs. At the same time, using electricity self-generation further offers possibilities for decreasing energy costs. In addition, energy flexibility can be gradually increased by on-site power storage, e.g., stationary batteries. As a consequence, both the electricity demand of the manufacturing system and the supply side, including battery storage, self-generation, and the energy market, need to be controlled in a holistic manner, thus resulting in a smart grid solution for industrial sites. This coordination represents a complex optimization problem, which additionally is highly stochastic due to unforeseen events like machine breakdowns, changing prices, or changing energy availability. This paper presents an approach to controlling a complex system of production resources, battery storage, electricity self-supply, and short-term market trading using multi-agent reinforcement learning (MARL). The results of a case study demonstrate that the developed system can outperform the rule-based reactive control strategy (RCS) frequently used. Although the metaheuristic benchmark based on simulated annealing performs better, MARL enables faster reactions because of the significantly lower computation costs for its own execution.

On an iterative process for the grid conjugation problem with iterations on the boundary of the solution discontinuity

An iterative process for the grid problem of conjugation with iterations on the boundary of the discontinuity of the solution is considered. Similar grid problem arises in difference approximation of optimal control problems for semilinear elliptic equations with discontinuous coefficients and solutions. The study of iterative processes for the states of such problems is of independent interest for theory and practice. The paper shows that the numerical solution of boundary problems of this type can be efficiently implemented using iterations on the inner boundary of the grid solution discontinuity in combination with other iterative methods for nonlinearities separately in each of the grid subregions. It can be noted that problems for states of controlled processes described by equations of mathematical physics with discontinuous coefficients and solutions arise in mathematical modeling and optimization of heat transfer, diffusion, filtration, elasticity theory, etc. The proposed iterative process reduces the solution of the initial grid boundary problem for a state with a discontinuous solution to a solution of two special boundary problems in two grid subdomains at every fixed iteration. The convergence of the iteration process in the Sobolev grid norms to the unique solution of the grid problem for each initial approximation is proved.

The Boundary Effect in the Accuracy Estimate for the Grid Solution of the Fractional Differential Equation

A grid method for solving the first boundary value problem for ordinary and partial differential equations with the Riemann–Liouville fractional derivative is justified. The algorithm is based on using Green’s function, the Fredholm integral equation, and the Lagrange interpolation polynomial. The impact of the Dirichlet boundary condition on the accuracy of the approximate solution is revealed and quantitatively described through the weight assessment. All the estimates provide clear evidence that the accuracy order of the grid method is higher near the boundary of the domain than it is in the inner nodes of the mesh set.

Reconfigurable Hardware Generation of Multigrid Solvers with Conjugate Gradient Coarse-Grid Solution

Not only in the field of high-performance computing (HPC), field programmable gate arrays (FPGAs) are a soaringly popular accelerator technology. However, they use a completely different programming paradigm and tool set compared to central processing units (CPUs) or even graphics processing units (GPUs), adding extra development steps and requiring special knowledge, hindering widespread use in scientific computing. To bridge this programmability gap, domain-specific languages (DSLs) are a popular choice to generate low-level implementations from an abstract algorithm description. In this work, we demonstrate our approach for the generation of numerical solver implementations based on the multigrid method for FPGAs from the same code base that is also used to generate code for CPUs using a hybrid parallelization of MPI and OpenMP. Our approach yields in a hardware design that can compute up to 11 V-cycles per second with an input grid size of 4096[Formula: see text]4096 and solution on the coarsest using the conjugate gradient (CG) method on a mid-range FPGA, beating vectorized, multi-threaded execution on an Intel Xeon processor.

Use of Hydrogen in Off-Grid Locations, a Techno-Economic Assessment

Diesel generators are currently used as an off-grid solution for backup power, but this causes CO2 and GHG emissions, noise emissions, and the negative effects of the volatile diesel market influencing operating costs. Green hydrogen production, by means of water electrolysis, has been proposed as a feasible solution to fill the gaps between demand and production, the main handicaps of using exclusively renewable energy in isolated applications. This manuscript presents a business case of an off-grid hydrogen production by electrolysis applied to the electrification of isolated sites. This study is part of the European Ely4off project (n° 700359). Under certain techno-economic hypothesis, four different system configurations supplied exclusively by photovoltaic are compared to find the optimal Levelized Cost of Electricity (LCoE): photovoltaic-batteries, photovoltaic-hydrogen-batteries, photovoltaic-diesel generator, and diesel generator; the influence of the location and the impact of different consumptions profiles is explored. Several simulations developed through specific modeling software are carried out and discussed. The main finding is that diesel-based systems still allow lower costs than any other solution, although hydrogen-based solutions can compete with other technologies under certain conditions.