Optimal Planning, Integration and Control of Smart Grids and Microgrids Systems

For 5G base stations equipped with multiple energy sources, such as energy storage systems (ESSs) and photovoltaic (PV) power generation, energy management is crucial, directly influencing the operational cost. Hence, aiming at increasing the utilization rate of PV power generation and improving the lifetime of the battery, thereby reducing the operating cost of the base station, a hierarchical energy management strategy based on the improved dung beetle optimization (IDBO) algorithm is proposed in this paper. The first control layer provides bus voltage control to each power module. In the second control layer, a dynamic balance control strategy calculates the power of the ESSs using the proportional–integral (PI) controller and distributes power based on the state of charge (SOC) and virtual resistance. The third control layer uses the IDBO algorithm to solve the DC microgrid’s optimization model in order to achieve the minimum daily operational cost goal. Simulation results demonstrate that the proposed IDBO algorithm reduces the daily cost in both scenarios by about 14.64% and 9.49% compared to the baseline method. Finally, the feasibility and effectiveness of the proposed hierarchical energy management strategy are verified through experimental results. Full article

This paper introduces a robust proportional integral derivative higher-order sliding mode controller (PID-HOSMC) based on a double power reaching law (DPRL) to enhance large-signal stability in DC microgrids. The microgrid integrates a solar photovoltaic (SPV) system, an energy storage system (ESS), and DC loads. Efficient DC-DC converters, including bidirectional and boost converters, are employed to maintain a constant voltage level despite the lower SPV output power. An artificial neural network (ANN) generates the optimal reference voltage for the SPV system. The dynamical model, which incorporates external disturbances, is initially developed and based on this model, and the PID-HOSMC is designed to control output power by generating switching gate pulses. Afterwards, Lyapunov stability theory is used to demonstrate the model’s closed-loop stability, and theoretical analysis indicates that the controller can converge tracking errors to zero within a finite time frame. Finally, a comparative numerical simulation result is presented, demonstrating that the proposed controller exhibits a 58% improvement in settling time and an 82% improvement in overshoot compared to the existing controller. Experimental validation using processor-in-the-loop (PIL) confirms the proposed controller’s performance on a real-time platform. Full article

Amidst the evolving paradigms of the contemporary energy landscape, marked by the imperative of sustainability and efficiency, the integration of energy storage has emerged as a transformative strategy that seeks to recalibrate the dynamics of electricity distribution and consumption. However, there remains a pressing need to determine the most economically viable approach for deploying energy storage solutions in residential low-voltage (LV) feeders, especially in rural areas. In this context, this paper presents the results of an economic evaluation of energy storage solutions for a residential LV feeder in a rural town in Australia. Specifically, the study compares the financial viability of a front-of-the-meter (FTM) battery installed on the feeder with that of a fleet of behind-the-meter (BTM) batteries. The FTM battery, with a size of 100 kW/200 kWh, is assumed to be operated by the retailer but owned by the community, with any profits assigned to the community. In this scenario, we studied a battery operating under standard network tariffs and three different trial tariffs that distribution network service providers currently offer in Australia. On the other hand, the fleet of BTM batteries (3 kW, 3.3 kWh) are individually owned by households with solar installations, and their cumulative capacity matches that of the FTM battery. The comparison is based on key economic parameters, including network charges, retail margins, frequency control ancillary service (FCAS) revenues, wholesale energy costs, technology costs associated with community batteries, and net profit or loss for the community, as well as considerations of utility grid arbitrage and solar photovoltaic (PV) self-consumption. The study also assumes different grant levels to assess the impact of subsidies on the economic feasibility for both battery configurations. The findings indicate that, while both require some form of subsidy for profitability, the BTM batteries outperform the FTM battery in terms of economic viability and so would require lower grant support. The FTM battery case finds a need for grants ranging from 75% to 95% to break even, while the BTM fleet requires approximately 50% in grants to achieve a similar outcome. In conclusion, this study highlights the importance of grant support in making energy storage solutions economically feasible. In particular, it highlights how the less mature segment of FTM batteries will need higher support initially if it is to compete with BTM. The outcomes of this study inform decision-making processes for implementing energy storage solutions in similar communities, fostering sustainable and cost-effective energy systems. Full article

The development of smart grids requires the active participation of end users through demand response mechanisms to provide technical benefits to the distribution network and receive economic savings. Integrating advanced machine learning tools makes it possible to optimise the network and manage the mechanism to maximise the benefits. This paper proceeds by forecasting consumption for the next 24 h using a recurrent neural network and by processing these data using a reinforcement learning-based optimisation model to identify the best demand response policy. The model is tested in a real environment: a portion of the Terni electrical distribution network. Several scenarios were identified, considering users’ participation at different levels and limiting the potential with various constraints. Full article

Sustainability is important in voltage regulation control in grids and must be done successfully. In this paper, a novel tilt-fractional order integral-derivative with a second order derivative and low-pass filters controller, referred to as TI^λDND²N² controller, is proposed to enhance the control performance of an automatic voltage regulator (AVR). In this article, the equilibrium optimizer (EO) algorithm is used to optimally determine the eight parameters of the proposed controller. In this study, a function consisting of time domain specifications is used as the objective function. To evaluate the performance of the proposed controller, it is compared with the proportional-integral-derivative (PID), fractional order PID (FOPID), PID accelerator (PIDA), PID plus second order derivative (PIDD²), and hybrid controllers used in previous studies. Then, Bode analysis is performed to determine the achievement of the proposed controller in the frequency domain. Finally, the robustness test is realized to assess the response of the proposed controller against the deterioration of the system parameters. As a result, the proposed controller demonstrates outstanding control performance compared to studies in terms of settling time, rise time and overshoot. The proposed controller shows superior performance not only in frequency domain analysis but also in perturbed system parameters. Full article

To enhance the reliability and flexibility of DC microgrids (DCMGs), this paper presents a decentralized power flow control strategy (PFCS) by using the transition operation modes. The transition operation modes are introduced as an effective communication method among power units, eliminating the use of additional digital communication links (DCLs) for the purpose of ensuring the power balance as well as the voltage regulation even under uncertain conditions. During the transition operation modes, the power unit which transmits the information shifts the DC-link voltage level, and the power unit which receives the information continuously monitors the DC-link voltage with predetermined time. When uncertain conditions occur in a particular power unit, this power unit triggers the transition operation modes to send this information to all power units in the DCMG system. The proposed PFCS can maintain the DC-link voltage at the nominal value for steady-state conditions both in the grid-connected mode and islanded mode. Moreover, the proposed PFCS significantly enhances the overall reliability of the decentralized DCMG system by effectively addressing several uncertainties stemmed from electricity price fluctuations, grid availability, battery state-of-charge (SOC) levels, and wind power variations. The scalability of the DCMG system is also demonstrated by incorporating an electric vehicle (EV) unit as an additional energy storage system (ESS). The EV unit seamlessly cooperates with the existing battery unit, functioning as additional ESS to regulate the DC-link voltage when the battery SOC level is low. Simulation and experimentation results under various conditions demonstrate the effectiveness of the proposed PFCS. Full article

The high-frequency modulation method (HFM) for a switched capacitor (SC) inverter often leads to high switching loss since it increases switching frequency. In order to reduce the switching frequency and switching loss in the HFM for SC inverter, this paper proposes a novel hybrid modulation strategy as well as a corresponding demo switched capacitor topology. The hybrid strategy limits the number of high-frequency switches, thus reducing the switching loss significantly. Meanwhile, the demo topology maintains the merits of current switched capacitor inverter such as low switch count, quadruple voltage-boosting ability and self-balancing capacity. The principle of the hybrid modulation method and the circuit configuration of the inverter are analyzed in detail. And comparisons are introduced to demonstrate the advantages of the proposed modulation method and topology. Finally, experimental results have proved the feasibility of the proposed inverter. Full article

A two-stage robust planning model is constructed in this paper, which can reduce the joint planning uncertainty of a wind-photovoltaic-energy storage system caused by the stochastic characteristics of renewable energy and ensure the sustainability of the power grid. Considering the life loss of energy storage system comprehensively, the joint planning is realized in the worst scenario. Addressing the problem that subjective and uniform robustness parameters in robust optimization cannot cope with the differentiated characteristics of each uncertainty, a robust microgrid-planning model and its modification strategy based on improved grey relational theory are proposed. The idea of weight distribution and dynamic value of identification coefficients are introduced into grey relational theory, so as to enhance the weight of indicators that influence planning and the relational degree between them, which can avoid the locally relational tendency. According to the relation degree, the renewable energy’s robustness parameters are modified to improve the applicability and flexibility of the microgrid-planning results. Finally, the effectiveness and superiority of the proposed theory and method are verified using a case study approach. Full article

The rapid development of electric vehicle (EV) technology and the consequent charging demand have brought challenges to the stable operation of distribution networks (DNs). The problem of the collaborative optimization of the charging scheduling of EVs and voltage control of the DN is intractable because the uncertainties of both EVs and the DN need to be considered. In this paper, we propose a deep reinforcement learning (DRL) approach to coordinate EV charging scheduling and distribution network voltage control. The DRL-based strategy contains two layers, the upper layer aims to reduce the operating costs of power generation of distributed generators and power consumption of EVs, and the lower layer controls the Volt/Var devices to maintain the voltage stability of the distribution network. We model the coordinate EV charging scheduling and voltage control problem in the distribution network as a Markov decision process (MDP). The model considers uncertainties of charging process caused by the charging behavior of EV users, as well as the uncertainty of uncontrollable load, system dynamic electricity price and renewable energy generation. Since the model has a dynamic state space and mixed action outputs, a framework of deep deterministic policy gradient (DDPG) is adopted to train the two-layer agent and the policy network is designed to output discrete and continuous control actions. Simulation and numerical results on the IEEE-33 bus test system demonstrate the effectiveness of the proposed method in collaborative EV charging scheduling and distribution network voltage stabilization. Full article

Considering the volatility and randomness of wind speed, this research suggests an improved hunter-prey optimization (IHPO) algorithm-based extreme learning machine (ELM) short-term wind power prediction model to increase short-term wind power prediction accuracy. The original wind power history data from the wind farm are used in the model to achieve feature extraction and data dimensionality reduction, using the partial least squares’ variable importance of projection (PLS-VIP) and normalized mutual information (NMI) methods. Adaptive inertia weights are added to the HPO algorithm’s optimization search process to speed up the algorithm’s convergence. At the same time, the initialized population is modified, to improve the algorithm’s ability to perform global searches. To accomplish accurate wind power prediction, the enhanced algorithm’s optimal parameters optimize the extreme learning machine’s weights and threshold. The findings demonstrate that the method accurately predicts wind output and can be confirmed using measured data from a wind turbine in Inner Mongolia, China. Full article

The insufficient amount of sample data and the uneven distribution of the collected data across faults are key factors limiting the application of machine learning in power transformer fault warning, as demonstrated by the poor adaptability of the established data-driven models under actual operating conditions. In this paper, an unsupervised and supervised learning method is designed for power transformer fault early warning based on electrical quantities and vibration signals. The method is based on the Fourier levels of transformer vibration signals under different electrical conditions measured in the field, and the vibration features are clustered according to their intrinsic properties by means of a spectral clustering algorithm. A decision tree model of the vibration characteristics under each cluster is then constructed to calculate early warning values for the transformer vibration spectrum under different electrical conditions, enabling the assessment of transformer production variability. The above process, which is based on field measurement data and data mining analysis methods, is cheaper than the existing transformer fault warning techniques at home and abroad and makes better use of information and training models. Full article

This paper provides a comprehensive overview of the microgrid (MG) concept, including its definitions, challenges, advantages, components, structures, communication systems, and control methods, focusing on low-bandwidth (LB), wireless (WL), and wired control approaches. Generally, an MG is a small-scale power grid comprising local/common loads, energy storage devices, and distributed energy resources (DERs), operating in both islanded and grid-tied modes. MGs are instrumental to current and future electricity network development, such as a smart grid, as they can offer numerous benefits, such as enhanced network stability and reliability, increased efficiency, an increased integration of clean and renewable energies into the system, enhanced power quality, and so forth, to the increasingly growing and complicated power systems. By considering several objectives in both islanded and grid-tied modes, the development of efficient control systems for different kinds of MGs has been investigated in recent years. Among these control methods, LB communication (LBcom)-based control methods have attracted much attention due to their low expenses, recent developments, and high stability. This paper aims to shed some light on different aspects, a literature review, and research gaps of MGs, especially in the field of their control layers, concentrating on LBcom-based control methods. Full article

The disordered access of massively distributed energy resources (DERs) brings great challenges to the operation stability of the power grid. This paper puts forward the concept of a cluster, which gathers DERs in large quantities, small capacities, dispersion and disorder to form a large, centralized and orderly whole, namely cluster, with certain incentive measures. In this paper, a multi-timescale optimization method of day-ahead planning and intra-day rolling optimization is proposed according to the characteristics of aggregated clusters and the requirements of China’s power grid architecture. Specifically, the day ahead model is proposed in two steps: the first step is to establish an optimization model with the goal of optimal fitting the target load curve and maximizing the utilization of DERs; The second step is to establish a potential game model considering the reasonable distribution of cluster benefits. Taking the minimum percentage of output correction of each cluster as the objective, considering the deviation of load forecasting and the deviation of day ahead instruction execution, an intra-day rolling optimization model is established. Finally, the application scenario of cluster participation in power grid auxiliary peak shaving is simulated and verified. The simulation results show that the cluster collaborative optimization method proposed in this paper can effectively reduce the load peak valley difference and maximize the use of cluster resources. The optimization tasks can be reasonably allocated while ensuring the stable and reliable operation of the power grid. Full article

In high frequency AC (HFAC) distribution system, the resonant inverter is used to improve power quality and keep the stability of the output AC voltage. Aiming at the problems of poor output power quality and slow transient performance caused by unreasonable filter parameter design and load change during inverter operation, a single-phase H-bridge LCLC resonant inverter based on analog circuit controller implement is introduced in this paper for HFAC power distribution system (PDS). In this study, to design harmonic compensator and analyze the responsiveness of the inverter, it is necessary to analyze the output voltage total harmonic distortion (THD) of LCLC resonant inverter and the performance of the open loop system in detail. On the one hand, a proportional-integral-resonant (PIR) controller is designed to maintain the zero static error of the voltage output and suppress the output voltage THD of LCLC resonant inverter. On the other hand, an integral controller combines with phase-shift modulation (PSM) method is presented to effectively improve the transient performance of resonant inverter and provide the fixed frequency of the output voltage. On the basis of the above, the experimental prototype is implemented with the output AC voltage root mean square of 28 V, and the output voltage frequency for resonant inverter is equal to switching frequency. A rated output power of 130 W experimental platform is built to verify the effectiveness of the theoretical analysis, control strategy, and modulation method. Full article