Seyed Saeid Mohtavipour’s research while affiliated with University of Guilan and other places

With the continued global rise in investments in renewable energy technologies, the share of these resources in electricity generation has significantly increased. While these technologies provide numerous benefits, such as reducing CO2 emissions, decreasing reliance on fossil fuels, and creating new jobs, there are growing concerns about their direct and indirect negative impacts on electricity markets. If policymakers aim to provide significant amounts of electricity from intermittent renewable sources, it will significantly affect the market, as these resources may not consistently meet demand due to their reliance on natural conditions. Therefore, the growing integration of renewable energy sources (RES) in a restructured electricity market has created challenges, which are explored in this paper. The results indicate that the presence of RES can lead to significant changes in market price behavior. A substantial share of RES in electricity generation capacity results in lower wholesale market prices, higher price spikes, and increased price volatility. Furthermore, an increased share of renewable energy sources may diminish the effectiveness of forward contracts in curbing market power within wholesale electricity markets. Also, the intermittent nature of renewable energy sources, such as solar and wind can increase the cost of ancillary services due to the greater need for storage and emergency supply.

The Nested Neutral Point Clamped (NNPC) converter, functioning as a voltage source Converter (VSC), provides an effective solution for applications requiring Medium-Voltage and High-Power (MVHP). Earlier implementations of this converter typically required many sensors to maintain capacitor voltages equally in all series Half-Bridge Sub-Modules (HB-SMs). This research introduces two methodologies to minimize sensor requirements for computing capacitor voltages using novel algorithms based on estimation methods. These strategies simplify the converter control procedure by eliminating individual current and voltage sensors specified for HB-SMs. The proposed approaches ensure precise voltage balance with minimal estimation error by continuously adjusting capacitor voltages using estimated values from previous iterations and switching signals. Extensive MATLAB Simulink tests verify the effectiveness of these techniques across diverse practical scenarios. The results highlight significant simplification of sensor complexity while maintaining strong performance in NNPC Converter applications, emphasizing the importance of sensor implementation in the cost and operational effectiveness of VSCs.

A promising solution for operations in the moderate voltage and high-power ranges, the Alternate Arm Multilevel Converter (AAMC) distinguishes itself with a distinctive topology as an AC-DC Voltage Source Converter (VSC). However, traditional implementations require numerous sensors to maintain capacitor voltage balancing across series Full-Bridge Sub-Modules (FB-SBs). Introducing an innovative approach to enhance AAMC efficiency, this study proposes two sensor reduction methods for calculating capacitor voltages with estimation algorithms, demonstrating the current paths within each method. These streamlined approaches simplify the control structure and eliminate the requirement for individual current sensors in each phase and voltage sensors per FB-SBs. By continuously adjusting capacitor voltages based on estimated values used by data from executed last steps and switching signals, the provided methods achieve precise voltage balance with minimal estimation error. The methods’ effectiveness is validated through extensive simulations achieved by MATLAB Simulink software across various operational conditions. The results demonstrate notable reductions in measurement component requirements while maintaining robust performance in AAMC applications. This research highlights the potential of optimizing sensor utilization to improve the performance of voltage source converter technologies in the future.

In recent years, the development of renewable energy technologies has gotten increasing attention due to the realization of cleaner production and high energy efficiency. Therefore, the proportion of these resources in electricity production has experienced a dramatic increase. However, with the features of intermittent nature and merit order effects, it is challenging to effectively optimize the integration of renewable energy sources (RES) into the electricity markets. To deal with the complex problem of selecting the proportionate share of non-dispatchable RES for participation in the market, this paper establishes a multi-criteria decision-making (MCDM) framework, which combines the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). Five criteria are defined and the corresponding mathematical model is solved by the proposed AHP-SAW method. In this way, the optimal share of renewable resources for participation in the wholesale market can be determined based on the desired criteria. Also, to compare the results obtained by the AHP-SAW method, the criteria weights are determined by Shannon entropy, and the problem is solved by the TOPSIS method. To assert the effectiveness of the proposed framework, two case studies are done. According to the results, by increasing the RES penetration level, the average price of the market decreases mildly while price volatility increases significantly. Also, the amount of forward contracts is decreased when the RES share increases. Moreover, the results show that a high amount of RES is not necessarily desirable and depends on the type of renewable resources and policymakers' priorities. The results for systems 1 and 2 indicate that the maximum allowable amount of RES are 85.359 and 38.24 MW, respectively.

While hydrogen energy is increasingly incorporated into energy hubs supplying the fuel cell electric vehicles, the question arose as to whether the inflexible hydrogen networks can provide a reliable supply for sustainable transportation. This study proposes a reliability contract method to capture the economic benefits of differentiated reliability according to the preferences of energy hubs for hydrogen outages. The innovative method divides the hydrogen network into multiple zones to construct the premiums and reimbursements for the reliability contract. According to the key factors of uncertainty in the energy hub operation problem, risk management and net premium principle, this research identifies the effective application of the zone-based reliability contract to hydrogen networks by using a stochastic programming model. Simulations implemented on a typical structure of hydrogen network with 5.2 h outage duration per year shows that the zone division of the proposed reliability contract effectively reduce the outage to 3.1 h.

Recently, the participation of wind sources in electricity markets has become a severe challenge due to their intermittent nature. Reconfiguration of power systems can effectively reduce the negative effects of uncertainties. So, this article presents a new method for participating in wind power plants and uncertain customers in a day‐ahead electricity market considering the reconfiguration process. This method tries to maximize social welfare through a two‐level optimization problem. To this end, uncertainties are modeled using the empirical cumulative distribution function and the Monte–Carlo method, and a probabilistic analysis of the market is performed. Then, by defining some indices to evaluate the participants’ satisfaction and using the analytic hierarchy process method (AHP), a new objective function is proposed so that its minimization leads to planning the system configuration and market participants to optimize mentioned indices. The proposed methodology also assumes that the participation of uncertain participants in the spot market will eliminate the imbalances caused by uncertainties. The simulations are implemented using real data on an 8‐bus sample network. The results confirm the efficiency of the proposed method in significantly reducing power producers’ and customers’ costs along with increasing total income and profit from the sale of energy.

Due to the stochastic nature of wind energy, allocating an appropriate investment incentive for wind generation technology (WGT) is a complicated issue. We propose an improvement on the traditional incentive, known as capacity payment mechanism (CPM), to reward the wind generators based on their performance exogenously affected by the wind energy potential of the location where the turbines are installed, and therefore, lead the investments towards locations with more generation potential. In CPM, a part of investment cost of each generator is recovered through fixed payments. However, in our proposal, wind generators are rewarded according to dynamic forecasts of the wind energy potential of the wind farm where they are located. We use an auto-regressive moving average (ARMA) model to forecast the wind speed fluctuations in long-term while capturing the auto-correlation of wind velocity variation in consecutive time intervals. Using the system dynamics (SD) modelling approach a competitive electricity market is designed to examine the efficiency of the proposed incentive. Performing a simulation analysis, we conclude that while a fixed CPM for wind generation can decrease the loss of load durations and average prices in long-term, the proposed improvement can provide quite similar results more efficiently.

Plug-in electric vehicles are capable of providing harmonic reference currents to the smart micro-grid in order to approximately neutralize the undesirable effects of the harmonic sources. This paper proposes a holistic multi-objective optimization framework for plug-in electric vehicles' involvement in accurately clearing the harmonic power market in a micro-grid. The proposed multi-objective framework is capable of concurrently optimizing contradict objectives, comprising total distortion payment function of the harmonic compensators, and mean of total harmonic distortion while satisfying the constraints associated with the network, vehicles, and market price. The payment function includes availability, loss, and loss of opportunity cost terms according to the harmonic capability curve of the plug-in electric vehicles and active power line conditioners. Moreover, this study proposes a novel hybrid algorithm consisting of the whale optimization, the gray wolf optimization, and the differential evolution algorithm for settling this new market framework. The best compromise solution is selected based on a particular preference among diverse Pareto optimal solutions on a fuzzy decision-making procedure basis. In order to illustrate the effectiveness of the proposed methodology and the outperformance of the plug-in electric vehicles compared with the active power line conditioners, the single-objective and the multi-objective harmonic power market under different circumstances are employed in this study.