This work proposes a novel heuristic-hybrid optimization method designed to solve the nonconvex economic dispatch problem in power systems. Due to the fast computational capabilities of the proposed algorithm, it is envisioned that it becomes an operations tool for both the generation companies and the TSO/ISO. The methodology proposed improves the overall search capability of two powerful heuristic optimization algorithms: a special class of ant colony optimization called API and a real coded genetic algorithm (RCGA). The proposed algorithm, entitled GAAPI, is a relatively simple but robust algorithm, which combines the downhill behavior of API (a key characteristic of optimization algorithms) and a good spreading in the solution space of the GA search strategy (a guarantee to avoid being trapped in local optima). The feasibility of the proposed method is first tested on a number of well-known complex test functions, as well as on four different power test systems having different sizes and complexities. The results are analyzed in terms of both quality of the solution and the computational efficiency; it is shown that the proposed GAAPI algorithm is capable of obtaining highly robust, quality solutions in a reasonable computational time, compared to a number of similar algorithms proposed in the literature.

Many real-life optimization problems often face an increased rank of nonsmoothness (many local minima) which could prevent a search algorithm from moving toward the global solution. Evolution-based algorithms try to deal with this issue. The algorithm proposed in this paper is called GAAPI and is a hybridization between two optimization techniques: a special class of ant colony optimization for continuous domains entitled API and a genetic algorithm (GA). The algorithm adopts the downhill behavior of API (a key characteristic of optimization algorithms) and the good spreading in the solution space of the GA. A probabilistic approach and an empirical comparison study are presented to prove the convergence of the proposed method in solving different classes of complex global continuous optimization problems. Numerical results are reported and compared to the existing results in the literature to validate the feasibility and the effectiveness of the proposed method. The proposed algorithm is shown to be effective and efficient for most of the test functions.

Capturing the dynamic behavior of the power distribution grids, especially under high penetration of renewables, is of high interest for grid operators. The distribution power grids are not fully observable due to lack of sufficient metering infrastructure, especially downstream of medium-voltage substations. Therefore, fusion of data recorded at significantly different reporting rates is proposed to increase the situational awareness of the system with non-negligible effect on the accuracy of the monitoring tool. Higher reporting rates are possible for next generation smart meters (SM), but they raise higher concerns about data privacy, already an issue for SM rollout. This article proposes a framework for knowledge extraction from high reporting-rate SM data. The process takes place at SM level and with low computation and communication costs and preserving user privacy, with the scope to increase the accuracy of the monitoring tools for distribution power grids. The methodology makes use of statistical metrics able to capture system dynamics relevant for network diagnosis. The proposed approach is validated on a three-phase low-voltage power flow model applied to a realistic testbed microgrid and real field measurements synchronized at 1 s.

As the complexity of electric systems increases, so does the required effort for the monitoring and management of grid operations. To solve grid performance issues, smart grids require the exchange of higher volumes of data, high availability of the telecommunication infrastructure, and very low latency. The fifth generation (5G) mobile network seems to be the most promising technology to support such requirements, allowing utilities to have dedicated virtual slices of network resources to maximize the service availability in case of network congestions. Regarding this evolving scenario, this work presents the Smart5Grid project vision on how 5G can support the energy vertical industry for the fast deployment of innovative digital services. Specifically, this work introduces the concept of network applications (NetApps), a new paradigm of virtualization that are envisioned to facilitate the creation of a new market for information technology (IT), small and medium enterprises (SMEs), and startups. This concept, and the open architecture that facilitates its implementation, is showcased by four real-life 5G-enabled demonstrators: (1) automatic fault detection in a medium voltage (MV) grid in Italy, (2) real-time safety monitoring for operators in high voltage (HV) substations in Spain, (3) remote distributed energy resources (DER) monitoring in Bulgaria, and (4) wide area monitoring in a cross-border scenario between Greece and Bulgaria.

Solid State Transformers (SST) may become, in the near future, key technological enablers for decentralized energy supply systems. They have the potential to unleash new technologies and operation strategies of microgrids and prosumers to move faster towards a low carbon-based economy. This work proposes a paradigm change in the hierarchically and distributed operated power systems where SSTs are used to asynchronously connect the many small low voltage (LV) distribution networks, such as clusters of prosumers or LV microgrids, to the bulk power system. The need for asynchronously coupled microgrids requires a design that allows the LV system to operate independently from the bulk grid and to rely on its own control systems. The purpose of this new approach is to achieve immune and resilient by design configurations that allow maximizing the integration of Local Renewable Energy Resources (L-RES). The paper analyses from the stability point of view, through simplified numerical simulations, the way in which SST-interconnected microgrids can become immune to disturbances that occur in the bulk power system and how sudden changes in the microgrid can damp out at the Point of Common Coupling (PCC), thus achieving better reliability and predictability in both systems and enabling strong and healthy distributed energy storage systems (DESSs). Moreover, it is shown that in a fully inverter-based microgrid there is no need for mechanical or synthetic inertia to stabilize the microgrid during power unbalances. This happens because the electrostatic energy stored in the capacitors connected behind the SST inverter can be used for a brief time interval, until automation is activated to address the power unbalance for a longer term.