cascaded multilevel inverter (CMI) is one of the most popular multilevel inverter topologies. This topology is synthesized with some series connected identical H-bridge cells. CMI requires several isolated dc sources which brings about some difficulties when dealing with this type of inverter. This paper addresses the problem by proposing a switched capacitor (SC) based CMI. The proposed topology, which is referred to as switched capacitor single source CMI (SCSS-CMI), makes use of some capacitors instead of the dc sources. Hence it requires only one dc source to charge the employed capacitors. Usually, the capacitor charging process in a SC cell is companied by some current spikes which extremely harm the charging switch and the capacitor. The capacitors in SCSS-CMI are charged through a simple auxiliary circuit which eradicates the mentioned current spikes and provides zero current switching condition for the charging switch. A computer-aid simulated model along with a laboratory-built prototype are adopted to assess the performances of SCSS-CMI, under different conditions.

In this paper an optimal probabilistic scheduling model of energy hubs operations is presented. The scheduling of energy hub determines the energy carriers to be purchased as input and converted or stored, in order to meet the energy requests, while minimizing the total hub's cost. However, as many other engineering endeavors, future operating criteria of energy hubs could not be forecasted with certainty. Load and price uncertainties are the most unclear parameters which hub operators deal with. In this regard, the paper proposes a 2m + 1 point estimation probabilistic scheduling scheme for energy hubs with multiple energy inputs and outputs.

Despite the increasing level of renewable power generation in power grids, fossil fuel power plants still have a significant role in producing carbon emissions. The integration of carbon capturing and storing systems to the conventional power plants can significantly reduce the spread of carbon emissions. In this article, the economic-emission dispatch of combined renewable and coal power plants equipped with carbon capture systems is addressed in a multiobjective optimization framework. The power system's flexibility is enhanced by hydropower plants, pumped hydro storage, and demand response program. The wind generation and load consumption uncertainties are modeled using stochastic programming. The dc power flow model is implemented on a modified IEEE 24-bus test system. Solving the problem resulted in an optimal Pareto frontier, while the fuzzy decision-making method found the best solution. The sensitivity of the objective functions concerning the generation-side is also investigated.

Multi-energy systems are flexible energy systems that can benefit from energy resources to supply different energy demands. Due to the capabilities of multi-energy systems in generating different energy carriers, these systems have been rapidly expanded in power systems. After restructuring in power system in recent years and appearance of competent energy markets, energy systems operated within such environments have been usually exposed to uncertainties of various parameters such as price, demand and etc. In this paper, a novel optimization framework based on hybrid scenario-based/interval/information gap decision theory (IGDT) method is developed to investigate optimal operation of smart energy hubs (S. E. Hubs) subject to economic priorities, technical constraints of distribution network and uncertainties. Considering energy hubs equipped to smart facilities, demand side management programs (DSMPs) including price response and load response services have been available to motivate electrical consumers to revise their consumption pattern in order to satisfy economic priorities of energy hubs. By using the results of employed hybrid uncertainty modeling approach, the operator of S. E. Hubs can decide either to take risk-averse or risk-seeking strategy against the uncertainties. Uncertainty based integration of S. E. Hubs into distribution network is evaluated regarding the IEEE 33-bus test system and the results obtained from simulations are presented for comparison.

At present, the power system is developing towards a fully renewable energy resources (RERs) equipped system due to significant challenges with the conventional units. This trend has led to remarkable new challenges for power system planners considering the stochastic nature of RERs, application of new emerging technologies, etc. This paper presents a two-stage robust stochastic programming model for optimal scheduling of commercial microgrids equipped with 100% RERs to handle the existing uncertainties. In the day-ahead electricity market, microgrids maximize their expected profits by optimizing their bidding strategy, while minimizing the imbalance cost is targeted for microgrids through adjusting the distributed energy resources in the real-time balancing market. Transactive energy technology is effectively applied to manage the energy trading. Simulation results on the IEEE 33-bus standard system integrated with microgrids verify that the proposed model provides satisfactory profits for microgrids participated in the energy exchanging process.

Recently, the decarbonization of the electric power system has led to substantial efforts for designing a pathway toward 100% renewable energy resources (RERs). This paper proposes a novel operational model for the effective participation of the interconnected microgrids with 100% RERs in the transactive energy market. The novelty of the proposed model is mostly related to the use of transactive energy technology for developing the free energy trading environment for the microgrids with 100% RERs as the local energy trading market to establish a dynamic energy balance in the system. To capture the intermittencies in the system, a hybrid IGDT/stochastic method is proposed in the deregulated environment. The proposed model is validated by selecting the modified IEEE 14-bus test system. The results indicate the effectiveness of the proposed model in providing the same percentage of cost-saving for microgrids when they simultaneously participate in the transaction energy market.