Abstract:To obtain efficient photovoltaic (PV) systems, optimum maximum power point tracking (MPPT) algorithms are inevitable. The efficiency of MPPT algorithms depends on two MPPT parameters, i.e., perturbation amplitude and perturbation period. The optimization of MPPT algorithms affect both the tracking speed and steady-state oscillation. In this paper, optimization methods of MPPT parameters are reviewed and classified into fixed and variable methods. The fixed MPPT parameters are constant during MPPT performance, and a trade-off should be made between the tracking speed and steady-state oscillation. However, the variable MPPT parameters will be changed to improve both the tracking speed and the steady-state oscillations. Moreover, some of them are simulated, compared, and discussed to evaluate the real contributions of the optimization methods to the MPPT efficiency. Furthermore, significant features of the optimization methods, i.e., noise immunity, robustness, and computation effort, are investigated.

Abstract:With the increasingly widespread of advanced metering infrastructure, electric load clustering is becoming more essential for its great potential in analytics of consumers’ energy consumption patterns and preference through data mining. Moreover, a variety of electric load clustering techniques have been put into practice to obtain the distribution of load data, observe the characteristics of load clusters, and classify the components of the total load. This can give rise to the development of related techniques and research in the smart grid, such as demand-side response. This paper summarizes the basic concepts and the general process in electric load clustering. Several similarity measurements and five major categories in electric load clustering are then comprehensively summarized along with their advantages and disadvantages. Afterwards, eight indices widely used to evaluate the validity of electric load clustering are described. Finally, vital applications are discussed thoroughly along with future trends including the tariff design, anomaly detection, load forecasting, data security and big data, etc.

Abstract:The sequential method is easy to integrate with existing large-scale alternating current (AC) power flow solvers and is therefore a common approach for solving the power flow of AC/direct current (DC) hybrid systems. In this paper, a high-performance graph computing based distributed parallel implementation of the sequential method with an improved initial estimate approach for hybrid AC/DC systems is developed. The proposed approach is capable of speeding up the entire computation process without compromising the accuracy of result. First, the AC/DC network is intuitively represented by a graph and stored in a graph database (GDB) to expedite data processing. Considering the interconnection of AC grids via high-voltage direct current (HVDC) links, the network is subsequently partitioned into independent areas which are naturally fit for distributed power flow analysis. For each area, the fast-decoupled power flow (FDPF) is employed with node-based parallel computing (NPC) and hierarchical parallel computing (HPC) to quickly identify system states. Furthermore, to reduce the alternate iterations in the sequential method, a new decoupled approach is utilized to achieve a good initial estimate for the Newton-Raphson method. With the improved initial estimate, the sequential method can converge in fewer iterations. Consequently, the proposed approach allows for significant reduction in computing time and is able to meet the requirement of the real-time analysis platform for power system. The performance is verified on standard IEEE 300-bus system, extended large-scale systems, and a practical 11119-bus system in China.

Abstract:With the extensive integration of high-penetration renewable energy resources, more fast-response frequency regulation (FR) providers are required to eliminate the impact of uncertainties from loads and distributed generators (DGs) on system security and stability. As a high-quality FR resource, community integrated energy station (CIES) can effectively respond to frequency deviation caused by renewable energy generation, helping to solve the frequency problem of power system. This paper proposes an optimal planning model of CIES considering FR service. First, the model of FR service is established to unify the time scale of FR service and economic operation. Then, an optimal planning model of CIES considering FR service is proposed, with which the revenue of participating in the FR service is obtained under market mechanism. The flexible electricity pricing model is introduced to flatten the peak tie-line power of CIES. Case studies are conducted to analyze the annual cost and the revenue of CIES participating in FR service, which suggest that providing ancillary services can bring potential revenue.

Abstract:It is economic and secure to determine the optimal siting and sizing of the offshore wind farms (OWFs) integrated into the AC system through voltage-source converter high-voltage direct current (VSC-HVDC) links. In this paper, an integrated planning model for the VSC-HVDC-link-based OWFs and the capacitors is proposed, where a decomposition technique is presented to solve the proposed mixed-integer nonlinear programming (MINLP) problem and obtain the optimal solution. This model can optimize the siting and sizing of the OWFs to improve the voltage profile and reduce the adverse influence of the reactive power of the OWFs. With the proposed planning model, the total investment costs, operation costs and maintenance costs of the OWFs, VSC-HVDC links, and the capacitors can be minimized. Simulations on the modified IEEE 118-bus system show that the proposed integrated planning model can provide more economic scheme than the independent planning scheme, in which the capacitors are planned after the OWFs. Besides, a series of sensitivity analysis on certain equipment costs are studied to obtain the regular pattern for sizing VSC stations.

Abstract:Considering the increasing integration of renewable energies into the power grid, batteries are expected to play a key role in the challenge of compensating the stochastic and intermittent nature of these energy sources. Besides, the deployment of batteries can increase the benefits of a renewable power plant. One way to increase the profits with batteries studied in this paper is performing energy arbitrage. This strategy is based on storing energy at low electricity price moments and selling it when electricity price is high. In this paper, a hybrid renewable energy system consisting of wind and solar power with batteries is studied, and an optimization process is conducted in order to maximize the benefits regarding the day-ahead production scheduling of the plant. A multi-objective cost function is proposed, which, on the one hand, maximizes the obtained profit, and, on the other hand, reduces the loss of value of the battery. A particle swarm optimization algorithm is developed and fitted in order to solve this non-linear multi-objective function. With the aim of analyzing the importance of considering both the energy efficiency of the battery and its loss of value, two more simplified cost functions are proposed. Results show the importance of including the energy efficiency in the cost function to optimize. Besides, it is proven that the battery lifetime increases substantially by using the multi-objective cost function, whereas the profitability is similar to the one obtained in case the loss of value is not considered. Finally, due to the small difference in price among hours in the analyzed Iberian electricity market, it is observed that low profits can be provided to the plant by using batteries just for arbitrage purposes in the day-ahead market.

Abstract:Recently, renewable power generation and electric vehicles (EVs) have been attracting more and more attention in smart grid. This paper presents a grid-connected solar-wind hybrid system to supply the electrical load demand of a small shopping complex located in a university campus in India. Further, an EV charging station is incorporated in the system. Economic analysis is performed for the proposed setup to satisfy the charging demand of EVs as well as the electrical load demand of the shopping complex. The proposed system is designed by considering the cost of the purchased energy, which is sold to the utility grid, while the power exchange is ensured between the utility grid and other components of the system. The sizing of the component is performed to obtain the least levelized cost of electricity (LCOE) while minimizing the loss of power supply probability (LPSP) by using recent optimization techniques. The results demonstrate that the LCOE and LPSP for the proposed system are measured at 0.038 $/kWh and 0.19% with a renewable fraction of 0.87, respectively. It is determined that a cost-effective and reliable system can be designed by the proper management of renewable power generation and load demands. The proposed system may be helpful in reducing the reliance on the over-burdened grid, particularly in developing countries.

Abstract:Cyber attacks are emerging threats in the Internet of Things applications, and power systems are typical cyber attack targets. As one of the most essential operation functions, frequency control is threatened by cyber intrusions, and the existing centralized control mode cannot effectively address cyber risks. In this study, a new distributed cyber-attack-tolerant frequency control scheme is designed. The distributed control mode also serves as a convenient tool for attack identification. The designed cyber-attack-tolerant frequency controller adopts the idea of passive fault attenuation, thus simplifying the design procedure. With the aid of graph theory and consensus techniques, distributed integral based and model predictive control (MPC) based controllers are designed. Compared with the integral type, the MPC-based controller can simultaneously improve the dynamic responses and the tolerance ability under attack. The proposed controller is validated via an IEEE benchmark system, and the effectiveness of its application in actual power systems is verified.

Abstract:This paper proposes a distributed real-time state estimation (RTSE) method for the combined heat and power systems (CHPSs). First, a difference-based model for the heat system is established considering the dynamics of heat systems. This heat system model is further used along with the power system steady-state model for holistic CHPS state estimation. A cubature Kalman filter (CKF)-based RTSE is developed to deal with the system nonlinearity while integrating both the historical and present measurement information. Finally, a multi-time-scale asynchronous distributed computation scheme is designed to enhance the scalability of the proposed method for large-scale systems. This distributed implementation requires only a small amount of information exchange and thus protects the privacy of different energy systems. Simulations carried out on two CHPSs show that the proposed method can significantly improve the estimation efficiency of CHPS without loss of accuracy compared with other existing models and methods.

Abstract:In order to reduce the risk of commutation failure (CF) in the AC/DC hybrid power system, the quantitative analysis on CF is required for on-line assessment and optimal control. This paper presents an accurate and reliable method to quantify the commutation security based on the trajectory due to the complexity of the high-voltage direct current (HVDC) model. Firstly, the characteristics of the extinction angle trajectory are analyzed under both commutation success and failure conditions. The commutation security margin index (CSMI) is then proposed for the HVDC systems. Moreover, a search strategy for parameter limits is put forward based on the sensitivity analysis of CSMI to accelerate the search speed with a guaranteed accuracy level. A modified IEEE 39-bus power system and an actual large-scale power system with 46 generators and 821 buses are utilized to verify the validity and robustness of the proposed index and strategy.

Abstract:The evolved capacitor commutated converter (ECCC), embedded with anti-parallel thyristors based dual-directional full-bridge modules (APT-DFBMs), can effectively reduce commutation failure (CF) risks of line-commutated converter-based high voltage direct current (HVDC) and improve the dynamic responses of capacitor-commutated converter-based HVDC. This paper proposes an improved coordinated control strategy for ECCC with the following improvements: ①under normal operation state, series-connected capacitors can accelerate the commutation process, thereby reducing the overlap angle and increasing the successful commutation margin; ② under AC fault conditions, the ability of ECCC to mitigate the CF issue no longer relies on the fast fault detection, since the capacitors inside the APT-DFBMs can consistently contribute to the commutation process and further reduce the CF probability; ③ the inserted capacitors can output certain amount of reactive power, increase the power factor, and reduce the required reactive power compensation capacity. Firstly, the proposed coordinated control approach is presented in detail, and the extra commutation voltage to mitigate the CFs provided by the proposed control approach and an existing approach is compared. Secondly, the mechanism of the improved control approach to accelerate commutation process and improve the power factor is analyzed theoretically. Finally, the detailed electromagnetic transient (EMT) simulation in PSCAD/EMTDC is conducted to validate the effectiveness of the proposed coordinated control. The results show that the proposed approach can present a further substantial improvement for ECCC, especially enhancing the CF mitigation effect.

Abstract:In this paper, the dynamic coupling between the wind turbine rotor speed recovery (WTRSR) and inertial response of the conventional virtual synchronous generator (VSG) controlled wind farms (WFs) is analyzed. Three distinguishing features are revealed. Firstly, the inertial response characteristics of VSG controlled WFs (VSG-WFs) are impaired by the dynamic coupling. Secondly, when the influence of WTRSR is dominant, the inertial response characteristics of VSG-WFs are even worse than the condition under which WFs do not participate in the response of grid frequency. Thirdly, this phenomenon cannot be eliminated by only enlarging the inertia parameter of VSG-WFs, because the influence of WTRSR would also increase with the enhancement of inertial response. A decoupling scheme to eliminate the negative influence is then proposed in this paper. By starting the WTRSR process after inertial response period, the dynamic coupling is eliminated and the inertial response characteristics of WFs are improved. Finally, the effectiveness of the analysis and the proposed scheme are verified by simulation results.

Abstract:The objective of this study is to develop a wind farm placement and investment methodology based on a linear optimization procedure. This problem has a major significance for the investment success for the projects of renewable energy such as wind power. In this study, a mesoscale approach is adopted whereby the wind farm location is investigated in comparison with a microscale approach where the location of each individual turbine is optimized. Specifical study focuses on the placement of a wind farm by economical optimization constrained by the power system, wind resources, and techno-economics. Linear optimization is introduced in this context at the power system which is constrained by wind farm planning.

Abstract:An optimal geographical location of wind turbines can ensure the optimum total energy output of a wind farm. This study introduces a new solution to the optimization of wind farm layout (WFLO) problem based on a three-step strategy and particle swarm optimization as the main method. The proposed strategy is applied to a certain WFLO to generate highly efficient optimal output power. Three case scenarios are considered to formulate the non-wake and wake effects at various levels. The required wind turbine positions within the wind farm are determined by the particle swarm optimization method. The rule of thumb, which determines the wind turbine spacing, is thoroughly considered. The MATLAB simulation results verify the proposed three-step strategy. Moreover, the results are compared with those of existing research works, and it shows that the proposed optimization strategy yields a better solution in terms of total output power generation and efficiency with a minimized objective function. The efficiencies of the three case studies considered herein increase by 0.65%, 1.95%, and 1.74%，respectively. Finally, the simulation results indicate that the proposed method is robust in WFLO design because it further minimizes the objective function.

Abstract:Due to nonlinear behavior of power production of photovoltaic (PV) systems, it is necessary to apply the maximum power point tracking (MPPT) techniques to generate the maximum power. The conventional MPPT methods do not function properly in rapidly changing atmospheric conditions. In this study, a fuzzy logic controller (FLC) optimized by a combination of particle swarm optimization (PSO) and genetic algorithm (GA) is proposed to obtain the maximum power point (MPP). The proposed FLC uses the ratio of power variations to voltage variations and the derivative of power variations to voltage variations as inputs and uses the duty cycle as the output. The range of changes in fuzzy membership functions and fuzzy rules are proposed as an optimization problem optimized by the PSO-GA. The proposed design is validated for MPPT of a PV system using MATLAB/Simulink software. The results indicate a better performance of the proposed FLC compared to the common methods.

Abstract:Identifying the parameters of photovoltaic (PV) modules is significant for their design and simulation. Because of the instabilities in the weather action and land surface of the earth, which cause errors in measuring, a novel fuzzy representation-based PV module is formulated and developed. In this paper, a novel locomotion-based hybrid salp swarm algorithm (LHSSA) is presented to identify the parameters of PV modules accurately and reliably. In the LHSSA, better leader salps based on particle swarm optimization (PSO) are incorporated to the traditional salp swarm algorithm (SSA) in a serialized scheme with the aim of providing more valuable information for the leader salps of the SSA. By this integration, the proposed LHSSA can escape the local optima as well as guide the seeking process to attain the promising region. The proposed LHSSA is investigated on different PV models, i.e., single-diode (SD), double-diode (DD), and PV module in crisp and fuzzy aspects. By comparing with different algorithms, the comprehensive results affirm that the LHSSA can achieve a highly competitive performance, especially on quality and reliability.

Abstract:In the study of the feasibility of solar tracking systems for crystalline silicon photovoltaic (PV) panels in hot and cold regions, it is argued recently that a tracking system is not necessary for sunbelt countries owing to the overheating that results from excessive exposure to solar irradiance. This conclusion has been formulated based on a mathematical model, which in turn is based on the assumption that the PV module temperature can be calculated using an empirical relation of this temperature to ambient temperature, available solar irradiance, and nominal operation cell temperature (NOCT). To support this conclusion, it is claimed that the mathematical model is validated experimentally. However, this assumption is vague and widely used in the literature. The objective of the present work is to reevaluate the above-mentioned assumption and to discuss the results deriving from it. It is shown experimentally in the present work that the above-mentioned assumption overestimates the PV module temperature. At a solar irradiance of 900 W/m², ambient temperature of 25 ℃, and wind speed of 5 m/s, the measured PV module temperature is lower than the value calculated based on the mentioned assumption by 29.26%.

Abstract:Distributed generation (DG) allocation in the distribution network is generally a multi-objective optimization problem. The maximum benefits of DG injection in the distribution system highly depend on the selection of an appropriate number of DGs and their capacity along with the best location. In this paper, the improved decomposition based evolutionary algorithm (I-DBEA) is used for the selection of optimal number, capacity and site of DG in order to minimize real power losses and voltage deviation, and to maximize the voltage stability index. The proposed I-DBEA technique has the ability to incorporate non-linear, nonconvex and mixed-integer variable problems and it is independent of local extrema trappings. In order to validate the effectiveness of the proposed technique, IEEE 33-bus, 69-bus, and 119-bus standard radial distribution networks are considered. Furthermore, the choice of optimal number of DGs in the distribution system is also investigated. The simulation results of the proposed method are compared with the existing methods. The comparison shows that the proposed method has the ability to get the multi-objective optimization of different conflicting objective functions with global optimal values along with the smallest size of DG.

Abstract:The optimal setting of directional overcurrent relays (DOCRs) ensures the fault detection and clearing in the minimum possible operation time. Directional protective relaying is carried out to coordinate relay settings in a meshed network in the presence of distributed generation. The main goal of DOCR coordination is to find the optimal time dial setting (TDS) and pickup multiplier setting (PMS) to reach the minimum total operation time of all primary relays in the presence of coordination constraints. Due to the complexity of mixed integer non-linear programming (MINLP) problem, imperialistic competition algorithm (ICA) as a powerful evolutionary algorithm is used to solve the coordination problem of DOCRs. The proposed DOCR coordination formulation is implemented in three different test cases. The results are compared with the standard branch-and-bound algorithm and other meta-heuristic optimization algorithms, which demonstrates the effectiveness of the proposed algorithm.

Abstract:With the gradual increase of distributed energy penetration, the traditional optimization model of distribution network can no longer guarantee the stable and efficient operation of the distribution network. In order to deal with the inevitable uncertainty of distributed energy, a new robust optimal operation method is proposed for active distribution network (ADN) based on the minimum confidence interval of distributed energy Beta distribution in this paper. First, an ADN model is established with second-order cone to include the energy storage device, capacitor bank, static var compensator, on-load tap changer, wind turbine and photovoltaic. Then, the historical data of related distributed energy are analyzed and described by the probability density function, and the minimum confidence interval is obtained by interval searching. Furthermore, via taking this minimum confidence interval as the uncertain interval, a less conservative two-stage robust optimization model is established and solved for ADN. The simulation results for the IEEE 33-bus distribution network have verified that the proposed method can realize a more stable and efficient operation of the distribution network compared with the traditional robust optimization method.

Abstract:Demand response (DR) has received much attention for its ability to balance the changing power supply and demand with flexibility. DR aggregators play an important role in aggregating flexible loads that are too small to participate in electricity markets. In this work, a DR operation framework is presented to enable local management of customers to participate in electricity market. A novel optimization model is proposed for the DR aggregator with multiple objectives. On one hand, it attempts to obtain the optimal design of different DR contracts as well as the portfolio management so that the DR aggregator can maximize its profit. On the other hand, the customers’ welfare should be maximized to incentivize users to enroll in DR programs which ensure the effective and flexible load control. The consumer psychology is introduced to model the consumers’ behavior during contract signing. Several simulation studies are performed to demonstrate the feasibility of the proposed model. The results illustrate that the proposed model can ensure the profit of the DR aggregator whereas the customers’ welfare is considered.

Abstract:Dynamic reactive power compensation equipment typically requires a fast response to output the necessary reactive power. The term “dynamic response time of reactive power” is often used but has never been clearly defined. This paper summarizes the reactive power calculations under different definitions and algorithms and considers these calculations in terms of signal processing to simulate and analyze the step response. This paper subsequently focuses on the widely used instantaneous reactive power algorithm and finally concludes that the dynamic reactive power response time closely depends on the reactive power calculation method itself. The single-phase instantaneous reactive power algorithm has the fastest response time. The reactive power response time of dynamic reactive devices in power systems is a minimum of a quarter of one cycle time for the well-known and widely used single-phase reactive power algorithms.

Abstract:Emerging technologies such as electric vehicles, solid-state transformers, and DC transformers are implemented using the closed-loop bi-directional dual-active-bridge (DAB) converter. In this context, it is necessary to have average models that provide an efficient way of tuning the proportional integral (PI) compensator parameters for large- and small-signal applications. We present a novel small-signal model (SSM) for DAB converter with a single closed-loop PI controller and the total elimination of reactive current (IQ=0). The method applies a modulation technique for IQ=0 and introduces a composite function in the controller while reducing the original non-linear switching model, which allows to decrease the order of the transfer function and analyze the closed-loop operation. The proposed SSM is analyzed using different response time, load, and DC voltage changes. The simulation and experimental results demonstrate the ease of implementation and effectiveness of the proposed model with respect to other SSM techniques.

Abstract:An improved fuzzy method is proposed to derive a fuzzy number for characterizing uncertain wind power. The input measurement data are firstly converted into nested sets, and the fuzzy number is further obtained based on nested set transformation method. Numerical studies have demonstrated the effectiveness and advantages of the improved fuzzy method.