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Concerns related to current harmonics have gained increased attention in recent years due to technological advancement in power electronics and the proliferation of non-linear loads in power distribution system. It has become challenging to limit current total harmonic distortion (THD) at consumer end mainly due to increased penetration of intermittent switching based non-linear loads. Shunt active power filter (SAPF) is considered a marvelous power electronics device to tackle consumer side current harmonics. This paper presents a synchronous reference frame (SRF) based improved control method for compliant working of SAPF to reduce current harmonics in the distribution network with same and different THD levels in three phases. This research work investigates an effectual DC link voltage control technique linked with SRF based control of SAPF to effectively mitigate current harmonics. A fuzzy logic-based automatic switch (FLBAS) is designed according to the THD standards of IET and IEEE for real-time controlling of SRF based control of SAPF. In addition, S-plane stability analysis of the proposed control scheme is performed using the model of SAPF's inverter. It is established that the closed-loop system is asymptotically stable with the proposed control scheme. The presented simulation results validate the effectiveness of the proposed control technique for SAPF robustly.

The distribution company (DISCO) determines optimal retail prices to operate the distribution network efficiently while promoting demand response (DR) programs. In addition, an energy storage system (ESS), which improves peak load management, is widely used for price-based DR. This paper proposes an electricity retail pricing strategy that considers the optimal operation of an ESS using a machine learning algorithm. An artificial neural network (ANN) is used to develop a practical model of the DR scheduling of an ESS. This model is trained using historical data that include the electricity price and the corresponding optimal demand obtained from the building energy management system. The proposed model is replicated using mathematical equations and directly integrated into the constraints of the retail pricing optimization problem of the distribution management system. The proposed ANN-based DR model of the ESS allows the development of an optimal pricing strategy with a single-level structure while reflecting the decision-making process of both the DISCO and the building operator. The proposed ANN-based DR model is verified through case studies, which prove that the model successfully expresses the price-optimal demand function and has high practical applicability. The results of the retail pricing demonstrate that the proposed strategy can accurately determine the balancing points while reducing the peak load.

Wind power forecasting plays a key role in the design and maintenance of wind power generation which can directly help to enhance environment resilience. Offshore wind power forecasting has become more challenging due to their operation in a harsh and multi-faceted environment. In this paper, the data generated from offshore wind turbines are used for power forecasting purposes. First, fragmented data is filtered and Deep Auto-Encoding is used to select high dimensional features. Second, a mixture of the CNN and LSTM models is used to train prominent wind features and further improve forecasting accuracy. Finally, the CNN-LSTM deep learning hybrid model is fine-tuned with various parameters for reliable forecasting of wind energy on three different offshore Windfarms. A state-of-the-art comparison against existing models is presented based on root mean square error (RMSE) and mean absolute error (MAE) respectively. The forecasting analyses indicate that the proposed CNN-LSTM strategy is quite successful for offshore wind turbines by retaining the lowest RMSE and MAE along with high forecasting accuracy. The experimental findings will be helpful to design environment resilient energy transition pathways.

For the purpose of increasing renewable energy penetration, Korean government and power utility have launched various incentive programs for renewable energy technologies. This paper proposes an optimal design for a campus microgrid at Seoul National University, South Korea, with the design objective is to maximize the project financial feasibility. The optimal design is obtained based on a techno-economic analysis which incorporates real market conditions. Significantly, this study considers various renewable energy incentives, particularly the incentive for energy storage systems (ESS) to discharge their energy during on-peak hours on weekdays, provided by Korea Electric Power Corporation. The major contribution of this study is to evaluate the impact of financial incentives on the optimal design, and their effectiveness in promoting higher renewable energy penetration. The results show that the optimal design produces a notable amount of energy savings with the help of incentives. Especially, the ESS incentive enhances the project financial feasibility with noticeably higher benefits. Furthermore, the results indicate that the ESS incentive determines the optimal microgrid design, with superior impact on ESS capacity and performance of economic dispatch strategies. Therefore, renewable energy incentives must be incorporated in the techno-economic analysis of a microgrid to archive an effective design.

Electric utility companies (EUCs) play an intermediary role of retailers between wholesale market and end-users, maximizing their profits. Retail pricing can be well deployed with the support of EUCs to promote demand response (DR) programs for heating, ventilating, and air-conditioning (HVAC) systems in commercial buildings. This paper proposes a pricing strategy to help EUCs and building operators achieve an optimal DR of price-elastic HVAC systems, considering peak load reduction. The proposed strategy is implemented by adopting a bi-level decision model. The nonlinear thermal response of an experimental building room is modeled using piecewise linear equations, which helps convert the bi-level model to the single-level model. The pricing strategy is implemented considering a time-of-use (TOU) pricing scheme, leading to low price volatility. Case studies are conducted for two types of load curves and the results demonstrate that the proposed strategy helps EUC promote the price-based DR of the commercial buildings for conventional load curves. However, EUC cannot reduce the peak load on duck curve caused by the large introduction of photovoltaic generators, even with price-sensitive HVAC systems in commercial building. This will be addressed in future studies by inducing DR participation of HVAC systems in residential buildings.

A distribution system was designed and operated by considering unidirectional power flow from a utility source to end-use loads. The large penetrations of distributed generation (DG) into the existing distribution system causes a variety of technical problems, such as frequent tap changing problems of the on-load tap changer (OLTC) transformer, local voltage rise, protection coordination, exceeding short-circuit capacity, and harmonic distortion. In view of voltage regulation, the intermittent fluctuation of the DG output power results in frequent tap changing operations of the OLTC transformer. Thus, many utilities limit the penetration level of DG and are eager to find the reasonable penetration limits of DG in the distribution system. To overcome this technical problem, utilities have developed a new voltage regulation method in the distribution system with a large DG penetration level. In this paper, the impact of DG on the OLTC operations controlled by the line drop compensation (LDC) method is analyzed. In addition, a generalized determination methodology for the DG penetration limits in a distribution substation transformer is proposed. The proposed DG penetration limits could be adopted for a simplified interconnection process in DG interconnection guidelines.

Nonlinearelectronicloadsdrawharmoniccurrentsfromthepowergridsthatcancause� energyloss,�missoperationofpowerequipment,�andotherseriousproblemsinthepowergrids.�This� paperproposesaharmoniccompensationmethodusingmultipledistributedresources�(DRs)�suchas� smalldistributedgenerators�(DGs)�andbatteryenergystoragesystems�(BESSs)�thatareintegratedto� thepowergridsthroughpowerinverters.�Forharmoniccompensation,�DRsshouldinjectadditional� apparentpowertothegridssothatcertainDRs,�especiallyoperatedinproximitytotheirratedpower,� maypossiblyreachtheirmaximumcurrentlimits.�Therefore,�intelligentcoordinationmethodsof� multipleDRsarerequiredforefficientharmoniccurrentcompensationconsideringthepowermargins� ofDRs,�energycost,�andthebatterystateofcharge.�Theproposedmethodisbasedonfuzzymulti� objectiveoptimizationsothatDRscancooperatewithotherDRstoeliminateharmoniccurrentswith� optimizingmutuallyconflictingmultiobjectives.���� �

This paper focuses on the control of bidirectional power flow in the electric shipboard power systems, especially in the Medium-Voltage Direct Current (MVDC) shipboard power system. Bidirectional power control between the main MVDC bus and the local zones can improve the energy efficiency and control flexibility of electric ship systems. However, since the MVDC system contains various nonlinear loads such as pulsed power load and radar in various subsystems, the voltage of the MVDC and the local zones varies significantly. This voltage variation affects the control performance of the bidirectional DC-DC converters as exogenous? disturbances. To improve the control performance regardless of uncertainties and disturbances, this paper proposes a novel controller design method of the bidirectional DC?C converters using L1 control theory and intelligent optimization algorithm. The performance of the proposed method is verified via large-scale real-time digital simulation of a notional shipboard MVDC power system.

Harmonic distortions in current waveform may cause significant problems in electric power system facility and operation. This paper presents an adaptive parameter estimation method to detect harmonic current components caused by nonlinear loads. In addition, a coordination strategy for multiple inverter-interfaced distributed generators to compensate the harmonic currents is discussed. The coordination strategy is realized by distributing the harmonic compensation participation index to individual distributed generators. The harmonic compensation participation index can be determined by the amount of remaining power generation capacity of each distributed generator. Simulation results based on switching-level inverter models show that the proposed harmonic detection method has good performance and the coordination strategy can improve harmonic problems efficiently.

This paper proposes a voltage control method based on a multi-agent system (MAS) for a distribution system to reflect the intentions of owners of distributed generators (DGs) with regard to voltage regulation. The proposed voltage control system consists of three types of agents: monitoring agents, DG agents, and a moderator. If a voltage violation is detected by a monitoring agent, DG agents send bidding data which represent the intentions of their owner to the moderator. The moderator determines the reactive power references of the DGs based on the voltage sensitivity and the bidding data. In order to interpret the complex relationship between the input and output and to reduce the calculation burden, a fuzzy inference system (FIS) is used for determining the reactive power reference. In addition, this paper proposes a method to compensate for voltage sensitivity. The proposed voltage scheme is validated by a pilot system.

This paper presents a voltage control method using multiple distributed generators (DGs) based on a multi-agent system framework. The output controller of each DG is represented as a DG agent, and each voltage-monitoring device is represented as a monitoring agent. These agents cooperate to accomplish voltage regulation through a coordinating agent or moderator. The moderator uses the reactive power sensitivities and margins to determine the voltage control contributions of each DG. A fuzzy inference system (FIS) is employed by the moderator to manage the decision-making process. An FIS scheme is developed and optimized to enhance the efficiency of the proposed voltage control process using particle swarm optimization. A simple distribution system with four voltage-controllable DGs is modeled, and an FIS moderator is implemented to control the system. Simulated data show that the proposed voltage control process is able to maintain the system within the operating voltage limits. Furthermore, the results were similar to those obtained using optimal power flow calculations, even though little information on the power system was required and no power flow calculations were implemented.

Spinel LiMn2O4 as a cathode material for lithium rechargeable batteries is economically prepared by a novel eutectic self-mixing method without any artificial mixing procedures of reactants. The phase transitions of lithium manganese oxide are found three times on the preparation. Thus, those process controls are discussed and emphasized. The prepared LiMn2O4 exhibits the initial discharge capacity of 124.8 mAh g−1 and the discharge capacity retention of 98.2% after 20 cycles.

Lithiated cathode materials are economically prepared using eutectic self-mixing method, spontaneous mixing of molten acetates and direct thermal reaction, without pulverization, grinding, agglomeration, morphology controlling, particle size controlling, and even any artificial stirring. When the chemical compounds with low melting point, such as lithium acetate and manganese acetate, are exposed to their eutectic temperature, they can be fluid substances by themselves without any solvents and spontaneous mixing. Using this method, we succeed in obtaining various lithiated cathode materials by just simple heat treatment. The discharge capacities of 149.5, 162.2, and 112.0 mA h/g for LiCoO2, LiNi0.8Co0.2O2, and LiMn2O4, respectively, prepared by this method demonstrate that the prepared materials are quite prospective as cathode materials for lithium rechargeable battery.

LiMO2 (M=Ni and Co) films were directly fabricated in a concentrated LiOH solution at fixed temperatures, between 100 and 200 °C, using Soft Solution Processing. Film formation mechanism studies elucidate the key role of hydroxyl group in obtaining the LiCoO2 films and of electrochemical anodic process in obtaining the LiNiO2 films. It is also found that the formation of the LiMO2 (M=Ni and Co) is fully based on dissolution–precipitation model. Successful preparation of LiMO2 (M=Ni and Co) films using the newly designed experimental conditions based on the elucidation of the microstructuring mechanism shows a good example of how to introduce more economical, energy and material efficient, and environment-friendly synthetic route.

Low-voltage direct current (LVDC) distribution systems have been evolving into interesting ways of integrating distributed energy resources (DERs) and power electronics loads to local distribution networks. In LVDC distribution systems, voltage regulation is one of the most important issues, whereas AC systems have concerns such as frequency, power factor, reactive power, harmonic distortion and so on. This paper focuses on a voltage control method for a LVDC distribution system based on the concept of multi-agent system (MAS), which can deploy intelligence and decision-making abilities to local areas. This paper proposes a distributed power flow analysis method using local information refined by local agents and communication between agents based on MAS. This paper also proposes a voltage control method by coordinating the main AC/DC converter and multiple DERs. By using the proposed method, we can effectively maintain the line voltages in a pre-defined normal range. The performance of the proposed voltage control method is evaluated by case studies and compared to conventional methods.