The rural electrification of Sub-Saharan Africa and South-East Asia is crucial to end the energy poverty in which around 1 billion people are trapped. Swarm electrification, i.e., the progressive building of decentralized and decarbonized electric infrastructure in a bottom-up manner, tackles rural electrification challenges by quickly providing modern and reliable electricity services to unelectrified communities while fostering local socio-economic development. This paper follows the technological approach of this electrification model and presents the development of a DC microgrid with decentralized production and storage suitable for rural electrification. This DC microgrid aims at interconnecting nanogrids, small collective autonomous power units composed of a solar panel and a lead–acid battery for 4 to 6 households, to increase the electrical services brought to the community and enhance the economic sustainability of this rural electrification model. The design of the proposed microgrid as well as its control algorithm are thoroughly addressed and tested from software simulations and experimental testing to field deployment in Madagascar. Extensive software, experimental and field-tests results are illustrated, and the microgrid design feedback is given. This paper overall validates the proper operation of the proposed microgrid, confirming the technical feasibility of the swarm electrification approach.

This paper proposes the description of an original Smart-Grids test bed aimed at teaching novel feeder automation functions to students from both university and industry origins. With this test bed, a lab class proposes to students first to develop feeder automation functions using scientific software and then to experiment by practically testing them on an emulated distribution grid platform, called PREDIS. This platform includes real medium voltage reduced scale loads, generators, and a supervisory control and data acquisition system. The presented lab class is part of a dedicated complete pedagogic module with lectures and experiments. Through the development, the tests and the deployments of their own solutions in an actual distribution grid, the students learn by doing from theory to practice the complete chain of Smart-Grids solutions: from the electrical to the communication layers.

Recently, DC-powered devices such as loads (USB plugs, chargers, LED lighting) and distributed energy resources (solar photovoltaic and battery energy storage) have been increasingly used. Therefore, their connection to the grid requires AC/DC converters, which raises the question of operating part of the grid in DC in order to connect DC loads to DC producers and storage. In Cambodia, the electrification rate is only about 82% of the population in 2021 in rural areas. The objective of this work is to propose a low voltage microgrid comprehensive planning tool for electrification of developing countries. From the data collected on consumption needs, the objective is to find the optimal electrification scheme, i.e., AC or AC/DC distribution, optimal topology and distributed energy resources allocation and operation for both grid-connected and off-grid mode. A set of technical, economic, and environmental key performance indicators allows for comparison of solutions. The interest and efficiency of such a tool are illustrated on a real case study, an island area. Moreover, uncertainties on load consumption are also considered to assess the sensitivity and robustness of the proposed algorithm. The results show that, although the overall cost of the hybrid AC/DC microgrid is slightly higher than that of the AC microgrid, it allows a gradual electrification avoiding large initial investments.

Planning the distribution network of the future involves forecasting the most likely scenario to make appropriate investment decisions. Many uncertainties concerning, e.g. the evolution of conventional loads, renewable production and electric vehicles (EVs) make it difficult to predict the location of the distribution network's weaknesses (overvoltages, undervoltages and overcurrents) and their occurrence. In some cases, alternative solutions such as demand response (DR) and reconfiguration can remove the constraints and prevent expensive network investment. This study proposes a two‐stage algorithm that is able to give the probability that no technical constraints will appear as a function of the reinforcement cost with and without using DR and/or reconfiguration. The first stage of the algorithm consists in running Monte Carlo simulations based on realistic scenarios for loads, EVs and renewable production development provided by French governmental roadmaps. The cost of reinforcement per line and per hour of constraints enables selection of the feeders, where DR (solved with linear programming) and/or reconfiguration (exhaustive research) will be implemented in the second stage of the algorithm to remove these constraints. The methodology is applied to a real part of a French distribution network.

As the integration of DC distributed resources increases in recent years, hybrid AC/DC topology emerges as a promising alternative in the planning of distribution systems. This article presents an AC/DC optimal expansion planning of distribution networks from Distribution System Operator's perspective with a special focus on grid architecture through Integer Programming-based topological constraints. The proposed formulation aims to determine the optimal location and sizing of converters and to select lines type (AC or DC) and state (closed or open). The problem is formulated as a mixed integer second-order conic programming model that guarantees global optimality and verifiable exactness by using off-the-shelf solvers. A multi-scenario approach with a Wasserstein distance-based scenario reduction is applied to deal with resource uncertainty. Case studies based on a 13 bus and IEEE 33 bus distribution networks were used to verify the effectiveness of the model. The results show that a hybrid architecture can improve the efficiency of the grid through optimal active power flow and reduce the total network costs.

Due to the rise of smart grids, new players and services are emerging and can have an impact on the decision-making process in distribution networks, which, over the past decades, were only driven by linear demand growth with a low level of uncertainties. Nowadays, the evolution of distribution networks and investment decisions (conductors and transformers) can no longer be based solely on deterministic assumptions of load evolution since there is a high level of uncertainties related to the development of electrical loads such as electric vehicles. In this paper, we focus on the uncertainty of the peak power, key elements for triggering an investment, and the flexibility to choose between different topologies of electric networks. To solve this problem, we apply a real option approach and provide an analytical model with closed-form solutions that allows a full treatment of the dynamic aspects of the decision to reconsider the topology of the network. Moreover, through a comparative statics analysis, we infer the sensitivity of the option value to modify the network with respect to the volatility of the peak power, the associated investment cost or other types of costs of power losses, the growth rate, or the discount rate.