Showing papers in "IEEE Transactions on Sustainable Energy in 2019"

Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting

Abstract: Wind speed forecasting is still a challenge due to the stochastic and highly varying characteristics of wind. In this paper, a graph deep learning model is proposed to learn the powerful spatio-temporal features from the wind speed and wind direction data in neighboring wind farms. The underlying wind farms are modeled by an undirected graph, where each node corresponds to a wind site. For each node, temporal features are extracted using a long short-term memory Network. A scalable graph convolutional deep learning architecture (GCDLA), motivated by the localized first-order approximation of spectral graph convolutions, leverages the extracted temporal features to forecast the wind-speed time series of the whole graph nodes. The proposed GCDLA captures spatial wind features as well as deep temporal features of the wind data at each wind site. To further improve the prediction accuracy and capture robust latent representations, the rough set theory is incorporated with the proposed graph deep network by introducing upper and lower bound parameter approximations in the model. Simulation results show the advantages of capturing deep spatial and temporal interval features in the proposed framework compared to the state-of-the-art deep learning models as well as shallow architectures in the recent literature.

Model Predictive Control of Bidirectional DC–DC Converters and AC/DC Interlinking Converters—A New Control Method for PV-Wind-Battery Microgrids

Abstract: In renewable energy systems, fluctuating outputs from energy sources and variable power demand may deteriorate the voltage quality. In this paper, a model predictive control strategy without using any proportional–integral–derivative (PID) regulators is proposed. The proposed strategy consists of a model predictive current and power (MPCP) control scheme and a model predictive voltage and power (MPVP) control method. By controlling the bidirectional dc–dc converter of the battery energy storage system based on the MPCP algorithm, the fluctuating output from the renewable energy sources can be smoothed while stable dc-bus voltage can be maintained. Meanwhile, the ac/dc interlinking converter is controlled by using the MPVP scheme to ensure stable ac voltage supply and proper power flow between the microgrid and the utility grid. Then, a system-level energy management scheme is developed to ensure stable operation under different operation modes by considering fluctuating power generation, variable power demand, battery state of charge, and electricity price. Compared with the traditional cascade control, the proposed method is simpler and shows better performance, which is validated in simulation based on a 3.5-MW PV-wind-battery system with real-world solar and wind profiles.

Short-Term Scheduling Strategy for Wind-Based Energy Hub: A Hybrid Stochastic/IGDT Approach

Abstract: This paper evaluates the scheduling problem for energy hub system consisting of wind turbine, combined heat and power units, auxiliary boilers, and energy storage devices via hybrid stochastic/information gap decision theory (IGDT) approach. Considering that energy hub plays an undeniable role as the coupling among various energy infrastructures, still it is essential to be investigated in both modeling and scheduling aspects. On the other hand, penetration of wind power generation is significantly increased in energy infrastructures in recent years. In response, this paper aims to focus on the hybrid stochastic/IGDT optimization method for the optimal scheduling of wind integrated energy hub considering the uncertainties of wind power generation, energy prices and energy demands explicitly in a way that not only global optimal solution can be reached, but also volume of computations can be lighten. In addition, by the proposed hybrid model, the energy hub operator can pursue two different strategies to face with price uncertainty, i.e., risk-seeker strategy and risk-averse strategy. This method optimizes energy hub scheduling problem in uncertain environment by mixed-integer nonlinear programming. This formulation is proposed to minimize the expected operation cost of energy hub where different energy demands of energy hub would be efficiently met. The forecast errors of uncertainties related to wind power generation and energy demands are modeled as a scenario, while an IGDT optimization approach is proposed to model electricity price uncertainty.

Hybrid Energy Sharing for Multiple Microgrids in an Integrated Heat–Electricity Energy System

Abstract: In order to facilitate energy sharing and improve system flexibility, a hybrid energy sharing framework of multiple microgrids (MGs) is proposed for a heat–electricity integrated energy system with combined heat and power (CHP) and demand response. First, considering the multi-timescale characteristics, an electrical and thermal energy sharing model of interconnected MGs with CHP and photovoltaic systems is built, in which CHP can operate in a hybrid mode by selecting the operating point flexibly. Moreover, the local subproblem of each MG is formulated and solved considering a comprehensive set of factors, including the generating heat and power cost, trading cost with utility grid, trading electrical and thermal energy cost with other MGs, load characteristic, power consumption utility, and thermal discomfort cost. In addition, a distributed optimization algorithm is used to solve the hybrid energy sharing problem, where the electrical and thermal energy prices can be obtained. Finally, the effectiveness of the proposed energy sharing method is demonstrated by a case study simulation.

Distributed Robust Energy Management of a Multimicrogrid System in the Real-Time Energy Market

Abstract: In this paper, a distributed robust energy management scheme for multiple interconnected microgrids (MGs) is developed. It aims to optimize the total operational cost of the MGs through energy trading with neighboring MGs and the main grid in the real-time energy market. Various uncertainties including renewable generation, load consumption, and buying/selling prices of the main grid are handled using an adjustable robust optimization technique. To keep consistent with the distributed nature of the multiple MGs, we propose a distributed adjustable robust optimal scheduling algorithm. Within the framework, each MG energy management system determines its own selling price and operation schedule via distributed communication of noncritical information with its neighboring MGs. Robust optimal scheduling and fair energy trading can be collectively achieved. A case study of a 4-MG system is conducted to validate the effectiveness of the proposed approach.

Robust Operation of Soft Open Points in Active Distribution Networks With High Penetration of Photovoltaic Integration

Abstract: Distributed generators including photovoltaic (PV) panels have been integrated dramatically in active distribution networks (ADNs). Due to the strong volatility and uncertainty, the high penetration of PV generation immensely exacerbates the conditions of voltage violation in ADNs. However, the emerging flexible interconnection technology based on soft open points (SOPs) provides increased controllability and flexibility to the system operation. For fully exploiting the regulation ability of SOPs to address the problems caused by PV, this paper proposes a robust optimization method to achieve the robust optimal operation of SOPs in ADNs. A two-stage adjustable robust optimization model is built to tackle the uncertainties of PV outputs, in which robust operation strategies of SOPs are generated to eliminate the voltage violations and reduce the power losses of ADNs. A column-and-constraint generation algorithm is developed to solve the proposed robust optimization model, which are formulated as second-order cone program to facilitate the accuracy and computation efficiency. Case studies on the modified IEEE 33-node system and comparisons with the deterministic optimization approach are conducted to verify the effectiveness and robustness of the proposed method.

The Small-Signal Stability Analysis of the Droop-Controlled Converter in Electromagnetic Timescale

Abstract: Recently, the converters controlled by droop controller with phase-locked loop were observed in islanded ac microgrids. However, only the state-space-based approach was applied to investigate the stability of this converter in the previous works. Compared with the state-space-based approach, the characteristic equation approach has plenty of advantages, such as convenient stability margin analysis (phase margin, gain margin, etc.) and simple stability criterion (Routh criterion). Thus, a novel small-signal modeling approach based on characteristic equation for converter-dominated ac microgrids is proposed to assess the system low-frequency stability in this paper. First, considering zero-order holder and time delay, the small-signal characteristic equation of this converter is presented by Pade approximation and dynamic phasor model. Furthermore, the implementation and parameter design of the converters are studied under the practical considerations. Compared with the existing characteristic equation methods, the proposed approach can verify that the performance is significantly improved. Eventually, simulations and experimental results are presented, indicating that the proposed approach can assess the system low-frequency stability conveniently and accurately.

Proactive Management of Microgrids for Resiliency Enhancement: An Adaptive Robust Approach

Abstract: Deploying microgrids ( $\boldsymbol {\mu G} \text{s}$ ) with the capability of islanding and self-supply can considerably enhance power system resilience in coping with weather events. To this end, practical methods are needed in order to operationally position and manage the $\boldsymbol {\mu G}$ to optimally control its resources and minimize the risk in the face of such disturbances. This paper proposes a two-stage adaptive robust formulation for $\boldsymbol {\mu G}$ predisturbance scheduling to minimize the damaging consequences of islanding events. The approach considers different uncertainties and obtains the best day-ahead schedule for the $\boldsymbol {\mu G}$ , which is robust against realization of uncertain parameters. An appropriate decomposition strategy, called column-and-constraint generation algorithm, is adopted to efficiently solve the problem, and “budget of uncertainty” parameters are introduced to control the conservatism of the robust solution. The effectiveness of the novel framework is evaluated and discussed on a $\boldsymbol {\mu G}$ using a set of illustrative case studies. The simulations show that the framework mitigates the huge cost of load shedding at the expense of a small increase in the predisturbance preparation cost. The advantages of our model are particularly remarkable when a weather event will come through, and a significant level of uncertainty exists.

Robust Two-Stage Regional-District Scheduling of Multi-carrier Energy Systems With a Large Penetration of Wind Power

Abstract: This paper proposes a robust day-ahead scheduling method for a multi-carrier energy system (MES), which would enhance the flexibility of power systems with a large sum of variable wind power. We build an MES model and propose an optimal MES schedule which helps MES reduce wind power curtailment in power systems. At first, electricity and natural gas networks are coordinated at the transmission (regional) level for accommodating the large penetration of wind power in regional MES. The distribution (district) level MES coordinates energy conversion and storage to jointly supply the electricity, natural gas, and heat loads. The transmission level MES is modeled using detailed network equations while the distribution level MES is modeled as a device with multiple input/output ports using the linear branch-flow-based energy hub model. A two-stage robust model is established to consider the variability of wind power at the two MES levels. The proposed problem is solved by a nested column-and-constraint (C&CG) generation method. The first-stage problem which schedules the hourly unit commitment is solved in the outer loop, while the inner loop solves the second-stage problem to realize the worst scenario. Several acceleration strategies are utilized to enhance the computational performance of the nested C&CG. Numerical results offered for a 6-bus 3-node system and a modified IEEE 118-bus 10-node system show the effectiveness of the proposed MES model and solution technique for enhancing the power system flexibility.

Robust Line Hardening Strategies for Improving the Resilience of Distribution Systems With Variable Renewable Resources

Abstract: Line hardening refers to strengthening certain distribution lines that will not be subject to outages during extreme conditions. Line hardening provides a resilient solution in the case of major faults in a radial or meshed distribution system. We propose a robust optimal line hardening method coupled with multiple provisional microgrids to improve the distribution system resilience against worst N-k contingencies. In this case, provisional microgrids, which are interconnected by hardened liens, do not have the islanding capability and depend on the supply provided by one or more electrically connected microgrids for islanding purposes. A trilevel optimal model is considered with the objectives of minimizing the costs of line hardening and the operation of multiple islanded provisional microgrids, which could include the cost of load shedding in each provisional microgrid considering the worst N-k contingencies. Specifically, a two-stage robust optimization model is formulated and Benders decomposition (BD) algorithm coupled with iterative relaxation procedure (IRP) is developed to enable the tractable computation. In our work, the master problem determines line hardening strategies and the subproblem discovers the impact of the worst N-k contingencies corresponding to the optimal operation of reformed provisional microgrids. The second-stage which includes the max–min subproblem is solved efficiently using linearization techniques, duality theory, and IRP. Our numerical results for the IEEE distribution test systems validate the effectiveness of the proposed model and reveal that distributed generation is critical in increasing the resilience of distribution systems corresponding to the worst N-k contingencies in provisional microgrids.

A Solar Time Based Analog Ensemble Method for Regional Solar Power Forecasting

Abstract: This paper presents a new analog ensemble method for day-ahead regional photovoltaic (PV) power forecasting with hourly resolution. By utilizing open weather forecast and power measurement data, this prediction method is processed within a set of historical data with similar meteorological data (temperature and irradiance), and astronomical date (solar time and earth declination angle). Furthermore, clustering and blending strategies are applied to improve its accuracy in regional PV forecasting. The robustness of the proposed method is demonstrated with three different numerical weather prediction models, the North American mesoscale forecast system, the global forecast system, and the short-range ensemble forecast, for both region level and single site level PV forecasts. Using real measured data, the new forecasting approach is applied to the load zone in Southeastern Massachusetts as a case study. The normalized root mean square error has been reduced by 13.80% to 61.21% when compared with three tested baselines.

Combination of Synchronous Condenser and Synthetic Inertia for Frequency Stability Enhancement in Low-Inertia Systems

Abstract: Inertia reduction due to high-level penetration of converter interfaced components may result in frequency stability issues This paper proposes and analyzes different strategies using synchronous condenser (SC), synthetic inertia (SI) of wind power plant, and their combination to enhance the frequency stability of low-inertia systems under various scenarios and wind conditions Furthermore, one of the SC models includes hardware of automatic voltage regulator (AVR) for better representation of the reality is implemented The simplified Western Danish power system simulated in real-time digital simulator is used as a test system of low inertia to demonstrate the effectiveness of the strategies The comparative results show that the combination of SC with AVR hardware-in-the-loop test and SI offers a better improvement not only on frequency stability (rate of change of frequency and frequency deviation) but also on the system synchronism under various operating conditions

LQR-Based Adaptive Virtual Synchronous Machine for Power Systems With High Inverter Penetration

Abstract: This paper presents a novel virtual synchronous machine controller for converters in power systems with a high share of renewable resources. Using a linear quadratic regulator-based optimization technique, the optimal state feedback gain is determined to adaptively adjust the emulated inertia and damping constants according to the frequency disturbance in the system, while simultaneously preserving a tradeoff between the critical frequency limits and the required control effort. Two control designs are presented and compared against the open-loop model. The proposed controllers are integrated into a state-of-the-art converter control scheme and verified through electromagnetic transient (EMT) simulations.

Operation Optimization of Power to Hydrogen and Heat (P2HH) in ADN Coordinated With the District Heating Network

Abstract: Increasing percentages of distributed generators in active distribution networks (ADNs) have increased the concern on excess generations in the medium and low voltage levels. High capacities of excess power are mainly from intermittent wind, solar power, and the restricted power generations of combined heat and power (CHP) plants, which are determined by the heat demand. Turning excess power to hydrogen has been recognized as a promising method to meet potential hydrogen demand from the traffic sectors. Nevertheless, it suffers from a low power-to-hydrogen efficiency, which is usually below 70% for commercialized electrolysis. By introducing in the heat recovery at the system level, we propose an integrated solution to supply heat to district heating networks (DHNs) while producing hydrogen, i.e., a power-to-hydrogen-and-heat scheme (P2HH). The extra benefit of P2HH resulting from heat supply is the reduction in the output of CHP, which reduces the excess energy in ADNs. This paper establishes a detailed “T-H-H” model to couple the power-to-heat and power-to-hydrogen processes. Then, a simplified P2HH dispatch model is proposed for the electricity-heat-hydrogen dispatch coordinated with ADN and DHN. Finally, the overall benefits of P2HH via improving the system economy and security are demonstrated with a modified IEEE 33-node system.

Assessing Increased Flexibility of Energy Storage and Demand Response to Accommodate a High Penetration of Renewable Energy Sources

Abstract: Today's power systems are subject to various challenges arising from the large-scale integration of renewable energy sources (RES), especially wind energy production. System flexibility, or the capability of a system to address deviations in variable RES production, is becoming more and more relevant. This paper aims to provide a systematic approach to evaluate the level of flexibility of a power system by unequivocally considering fast-ramping units (FRU), hourly demand response (DR) and energy storage (ES). In addition, to research the flexibility role in power system operation, an “online” index is considered to evaluate the technical aptitude of the FRU, hourly DR and ES system to deliver the required flexibility. The mathematical representation of day-ahead scheduling, with the added modeling of an online flexibility index, is a mixed-integer nonlinear program (MINLP). This paper presents a method to convert this MINLP into a mixed-integer linear program without loss of accuracy. The adapted 6-bus and IEEE 118-bus systems are employed to assess the suggested models and flexibility metric, demonstrating the proficiency of the online flexibility index.

Modeling Type-4 Wind in Weak Grids

Abstract: An existing wind power plant at ERCOT experienced poorly damped and undamped low-frequency oscillations at $\text{3}\sim \text{4}$ Hz under weak grid condition. The objective of this paper is to shed the insight of the oscillation mechanism through linear system analysis. Two simplified models are developed and compared against a detailed model for Type-4 wind with weak grid interconnection. The detailed model includes grid-side converter's outer power/voltage control, inner current controls, phase-locked loop (PLL), and transmission line electromagnetic dynamics. The first simplified model uses a first-order delay to replace the current control loop and ignores the transmission line dynamics and PLL. The second simplified model uses the same assumptions except that PLL is considered. Linearized system block diagrams for the two simplified models are derived and compared. The mechanism of the low-frequency oscillations are explained using eigenvalue analysis and the Root-Locus method. The root causes are identified as weak grid, high wind power export, low voltage, and low PLL bandwidth. Further, the simplified model considering PLL dynamics is more accurate in low-frequency oscillation mode identification and system stability prediction.

A Multi-Model Combination Approach for Probabilistic Wind Power Forecasting

Abstract: Short-term probabilistic wind power forecasting can provide critical quantified uncertainty information of wind generation for power system operation and control. It would be difficult to develop a universal forecasting model dominating over other alternative models because of the inherent stochastic nature of wind power. Therefore, a novel multi-model combination (MMC) approach for probabilistic wind power forecasting is proposed in this paper to exploit the advantages of different forecasting models. The proposed approach can combine different forecasting models those provide different kinds of probability density functions to improve the performance of probabilistic forecasting. Three probabilistic forecasting models based on the sparse Bayesian learning, kernel density estimation, and beta distribution fitting are used to form the combined model. The parameters of the MMC model are solved by two-step optimization. Comprehensive numerical studies illustrate the effectiveness of the proposed MMC approach.

Dispatch Model for CHP With Pipeline and Building Thermal Energy Storage Considering Heat Transfer Process

Abstract: Utilizing the flexibility provided by the thermal system components, for example, pipelines in the district heating network (DHN), building envelopes as well as thermal energy storage (TES) devices, can be an effective way for power system to solve the wind curtailment problem which closely relates to the limited flexibility of combined heat and power. However, almost all the previous studies ignored heat transfer (HT) constraints when modeling these abovementioned thermal system components. An HT constraint is one of the most important constraints in thermal system analysis, without considering it may result in infeasibility for the dispatch results. Hence, in this study, we propose a detailed model for these three components, namely DHN, building envelopes, and TES devices, with consideration of HT constraints. This model can be further simplified to consider different combinations of these components by setting relevant model parameters. An iteration solution to deal with such nonlinear HT constraints is put forward. The effectiveness of the proposed model is verified by case studies. The simulation results demonstrate that considering HT constraints is necessary when try to exploit the flexibility provided by these thermal system components, or we may overestimate their benefit.

Optimal Real-Time Operation Strategy for Microgrid: An ADP-Based Stochastic Nonlinear Optimization Approach

Abstract: This paper proposes an approximate dynamic programming (ADP) based algorithm for the real-time operation of the microgrid under uncertainties. First, the optimal operation of the microgrid is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem, combining the ac power flow and the detailed operational character of the battery. For this NP-hard problem, the proposed ADP based energy management algorithm decomposes the original multitime periods MINLP problem into single-time period nonlinear programming problems. Thus, the sequential decisions can be made by solving Bellman's equation. Historical data is utilized offline to improve the optimality of the real-time decision, and the dependency on the forecast information is reduced. Comparative numerical simulations with several existing methods demonstrate the effectiveness and efficiency of the proposed algorithm.

A Combined Algorithm for Cleaning Abnormal Data of Wind Turbine Power Curve Based on Change Point Grouping Algorithm and Quartile Algorithm

Abstract: There exist plenty of outliers in power curve of wind turbines, which is not conducive to the follow-up information mining. Improving the wind power curve data quality of wind turbines has great engineering value. According to the spatial distribution characteristics and shapes, the outliers of wind power curve are divided into four types: the bottom curve, the mid curve, and the top curve stacked outliers, as well as scattered outliers around the curve. Based on the outlier distribution characteristics, this paper proposes a method and process of cleaning the outliers based on the change point grouping algorithm and the quartile algorithm, followed by a theoretical analysis of its feasibility. Case studies and its comparisons with the quartile–change point grouping algorithm and the local outlier factor algorithm show that the proposed change point grouping–quartile algorithm can effectively identify the four types of outliers, with good cleaning effect, high efficiency, and strong versatility.

An Efficient Planning Algorithm for Hybrid Remote Microgrids

Abstract: This paper presents an efficient planning algorithm for microgrids in remote isolated communities. Different from the existing research that assumes a specific microgrid topology, we propose a planning algorithm that jointly specifies the optimal grid topology, namely AC, DC, or hybrid AC/DC, along with the optimal locations and sizes of distributed energy resources, energy storage systems, and AC–DC converters. The planning objective is to ensure reliable power flow with minimum deployment and operational costs. The planning problem is formulated as a mixed integer nonlinear program, and given the complexity of the problem, the proposed algorithm implements a two-stage framework that results in an efficient planning solution. The first stage deals with the specification of the microgrid topology, and allocation and sizing of all the equipment following a heuristic optimization approach. Upon deciding the microgrid topology and equipment installation in the first stage, the second stage of the planning algorithm ensures smooth and reliable operation for the proposed topology over all possible operation scenarios. This is achieved with minimal operational costs by considering the optimal nonlinear scheduling problem for the installed equipment. Test cases are presented to investigate the performance of the proposed planning algorithm at different fuel transportation cost scenarios.

Day-Ahead Dispatch of Integrated Electricity and Natural Gas System Considering Reserve Scheduling and Renewable Uncertainties

Abstract: For secure operation of integrated electricity and natural gas system (IEGS), reserve is a useful support to manage renewable uncertainties and N − 1 contingencies. Thus, a day-ahead economic dispatch model of IEGS with reserve scheduling is presented in this paper. Considering the uncertainty of gas flow direction, a novel second-order cone (SOC) relaxation of Weymouth equation is designed to address the nonconvexity. Then, the proposed robust nonconvex model is mathematically transformed into a solvable mixed integer second-order cone programming (MISOCP) problem. To guarantee the tightness of SOC relaxation and achieve accurate dispatch solutions, MISOCP results are corrected accordingly by the multi-slack-node gas flow calculation with the Newton–Raphson method. Numerical cases are performed on IEEE 39-bus-15-node and IEEE 118-bus-40-node test IEGSs, demonstrating the proposed approach is feasible and effective for exact IEGS day-ahead dispatch and the proposed MISOCP model can provide a more economic dispatch solution with a shorter computational time than conventional MISOCP and mixed integer linear programming (MILP) models.

Wind Power Curve Modeling and Wind Power Forecasting With Inconsistent Data

Abstract: Wind power curve modeling is a challenging task due to the existence of inconsistent data, in which the recorded wind power is far away from the theoretical wind power at a given wind speed. In this case, confronted with these samples, the estimated errors of wind power will become large. Thus, the estimated errors will present two properties: heteroscedasticity and error distribution with a long tail. In this paper, according to the above-mentioned error characteristics, the heteroscedastic spline regression model (HSRM) and robust spline regression model (RSRM) are proposed to obtain more accurate power curves even in the presence of the inconsistent samples. The results of power curve modeling on the real-world data show the effectiveness of HSRM and RSRM in different seasons. As HSRM and RSRM are optimized by variational Bayesian, except the deterministic power curves, probabilistic power curves, which can be used to detect the inconsistent samples, can also be obtained. Additionally, with the data processed by replacing the wind power in the detected inconsistent samples with the wind power on the estimated power curve, the forecasting results show that more accurate wind power forecasts can be obtained using the above-mentioned data processing method.

Multi-Time-Scale Rolling Optimal Dispatch for AC/DC Hybrid Microgrids With Day-Ahead Distributionally Robust Scheduling

Abstract: To cope with the impact of predicted source-load deviations on the optimal dispatch of ac/dc hybrid microgrids at different time scales, this paper develops a multiple-time-scale (MTS) rolling optimal dispatching framework A novel day-ahead distributionally robust optimization (DRO) model, based on the predicted means, deviations, and confidence probabilities of the source-load power, reduces the conservativeness of the adaptive robust optimization and provides robust day-ahead scheduling plans The source-load deviations are effectively compensated by an MTS rolling optimization for the intraday dispatch, which adjusts the operating power of the controllable units Relaxed penalty cost functions and rigid constraints on the state of charge are added to ensure cyclic regulation of the energy storage The superiority and validity of the day-ahead DRO model and the intraday MTS rolling optimization model are verified in case studies

Active Power Control of Wind Turbine Generators via Coordinated Rotor Speed and Pitch Angle Regulation

Abstract: With increased wind power penetration in modern power systems, wind turbine generators (WTG) are expected to provide the active power control (APC) for tracking a desired power reference from system or wind farm operators. In practice, the pitch angle control (PAC) and the rotor speed control (RSC) methods can be used for APC in variable-speed variable-pitch WTGs, but the latter using turbine inertia as energy buffer is more attractive due to less pitch activation and higher wind energy production. For existing RSC methods, when the rotor speed reaches the upper speed limit at high wind speed or low power reference, they will actually become PAC to follow active power command, which also results in frequent pitch angle manipulation and considerable fatigue on the pitch servo system. To overcome this drawback, this paper proposes a new APC strategy integrating the rotor speed and pitch angle regulation. By utilizing the kinetic energy of rotor inertia at any pitch position (zero as well as non-zero pitch angle), this strategy can effectively avoid frequent action of pitch actuator while sustaining dispatched active power. The proposed method is verified by the fatigue, aerodynamics, structures, and turbulence-based simulations and wind turbine simulator-based experiments.

Optimal Scheduling of the Regional Integrated Energy System Considering Economy and Environment

Abstract: With the reformation of energy market and the development of energy intelligent technology, regional integrated energy system, as an important direction of energy system development, is tending to play an important role in the field of energy supply and demand response program. This paper presents an optimal operation model of an integrated energy system considering the response of energy price. In this paper, the typical structure of the integrated energy system and the model of each system module are first introduced in detail, and the system model presented includes three subsystems: power supply, heating, and cooling. Second, a complete scheduling scheme is built based on the energy consumption characteristics and system operation characteristic of the integrated energy system. The model presented could reduce the cost of system without causing a significant amount of environmental pollutions, and improve the energy efficiency of the system efficiently. Besides, the scheduling strategy proposed could also provide support for the operation strategy of the integrated energy system under the energy development.

Optimization of Battery Energy Storage to Improve Power System Oscillation Damping

Abstract: This paper studies the optimization of both the placement and controller parameters for Battery Energy Storage Systems (BESSs) to improve power system oscillation damping. For each BESS, dynamic power output characteristics of the power converter interface are modeled considering the power limit, State of Charge limit, and time constant. Then, a black-box mixed-integer optimization problem is formulated and tackled by interfacing time-domain simulation with a mixed-integer Particle Swarm Optimization algorithm. The proposed optimization approach is demonstrated on the New England 39-bus system and a Nordic test system. The optimal results are also verified by time-domain simulation. To improve the applicability and efficiency of the proposed method, seasonal load changes and the minimum number of BESS units to be placed are considered. The proposed controller is also compared to other controllers to validate its performance.

MPC-Based Coordinated Voltage Regulation for Distribution Networks With Distributed Generation and Energy Storage System

Abstract: This paper presents a model predictive control (MPC)-based coordinated voltage control scheme for distribution networks with high penetration of distributed generation (DG) and energy storage. In this scheme, the DG units, energy storage devices, and on-load tap changer are optimally coordinated to maintain all bus voltages in the network within a permissible range. To better coordinate the economical operation and voltage regulation, two control modes are designed according to the operating conditions. In the preventive mode, the DG units operate in the maximum power point tracking mode. State-of-charge of energy storage system (ESS) units and power outputs of DG and ESS units are optimized while maintaining the voltages within the feasible range. In the corrective mode, active power curtailment of DG units is also used as a necessary method to correct the severe voltage deviations. The voltage sensitivity coefficients with respect to the power injections and tap changes are updated in real time using an analytical sensitivity calculation method to improve the computation efficiency. A test system consisting of two 20-kV feeders fed from the same substation based on a real distribution network was used to validate the proposed coordinated voltage control scheme under both normal and large-disturbance conditions.

Privacy-Preserving Optimal Dispatch for an Integrated Power Distribution and Natural Gas System in Networked Energy Hubs

Abstract: Energy hubs (EHs) are deployed to supply multi-energy demands economically in the integrated energy systems while considering physical and operational constraints of individual energy systems. This paper presents a decentral framework for the optimal dispatch of integrated power distribution and natural gas system (IDGS) in networked energy hubs. The IDGS operator (IDGSO) is charged with delivering a constrained optimal dispatch while preserving the privacy of participants’ data. First, a comprehensive optimization model is proposed in this paper using the mixed-integer second-order cone programming and sequential second-order cone (SOC) algorithms to guarantee the exactness of SOC relaxation. In this optimization process, we consider advanced control methods, such as remotely controlled switches and static var compensators, and propose two improvements including initial point setting and a special type of cutting plane to obtain a fast and feasible solution. Next, we apply Benders decomposition for decentralizing the decision-making process in accordance with the practical leader–follower relationship between IDGSO and EHOs (EH operators) and preserving private operation data of individual EHOs. Finally, we present numerical case studies, which illustrate the effectiveness of the proposed iterative model and solution algorithm for enhancing the operation of EHs.

Control of Flywheel Energy Storage Systems in the Presence of Uncertainties

Abstract: In this paper, an optimal nonlinear controller based on model predictive control (MPC) for a flywheel energy storage system is proposed in which the constraints on the system states and actuators are taken into account. In order to control the system in the presence of modeling uncertainties and under the influence of external disturbances, tube-based MPC is utilized as a robust controller. Simulation results demonstrate the merits of the proposed method in controlling the dc link voltage and the fly wheel speed.