Abstract:We aim to systematically review challenges imposed by emerging distributed energy resources (DERs) to model in two basic distribution management system (DMS) online applications—power flow and short-circuit analysis, as well as to offer a systematic review of potential solutions. In the last decade, electronically coupled DERs became increasingly popular. DERs can employ a wide range of control strategies for power, current, or voltage control, in both normal and faulted conditions. Therefore, DERs cannot be modeled with the traditional PQ (load or generator bus), and PV (generator bus) bus types used for modeling synchronous and induction machines in online power flow calculations. Moreover, because fault currents of DERs are limited to predefined maximal values, electronically coupled DERs cannot be represented with traditional voltage source behind impedance models for online short-circuit calculation (SCC). However, most of the DMS software packages still use the traditional models to represent all DER types, including those that are electronically coupled. This paper shows that there will be high calculation errors in such practice, which makes the system model be an inadequate representation of the system. And this will lead to serious errors in managing, control, and operation of distribution systems. Nonetheless, potential solutions to the challenges are systematically reviewed. Finally, calculation results on a distribution test system with all DER types are used to prove the claim.

Abstract:One purpose of stimulating financing and investment through private capital is to absorb a higher proportion of renewables and promote renewable industry development. This paper first reviews the current overall situation of renewables financing and investment, and further analyzes the policy environment with respect to the development plan, regulation and special funds. Based on the analysis of the status quo, the paper then discusses the internalities and the externalities that have driven the changes of private capital investment in renewable energy projects, illustrated by a strengths weaknesses pportunities threats (SWOT) analysis. An ideal financing model, public–private partnership and distributed energy resources pattern are analyzed to identify key arrangements and design proper development schemes for both private investors and the government. If China can overcome the defects and obstacles in a reasonable and orderly fashion, the financing and investment problem of China’s renewables industry will be solved in many ways. Private capital in the Chinese renewable energy market will bring great incentive if the entire industry can select some promising sub-industries in the renewables sector and choose some appropriate operation modes.

Abstract:A solution to the power flow problem is imperative for many power system applications and several iterative approaches are employed to achieve this objective. However, the chance of finding a solution is dependent on the choice of the initial point because of the non-convex feasibility region of this problem. In this paper, a non-iterative approach that leverages a convexified relaxed power flow problem is employed to verify the existence of a feasible solution. To ensure the scalability of the proposed convex relaxation, the problem is formulated as a sparse semi-definite programming problem. The variables associated with each maximal clique within the network form several positive semidefinite matrices. Perturbation and network reconfiguration schemes are employed to improve the tightness of the proposed convex relaxation in order to validate the existence of a feasible solution for the original non-convex problem. Multiple case studies including an ill-conditioned power flow problem are examined to show the effectiveness of the proposed approach to find a feasible solution.

Abstract:This paper introduces a robust sparse recovery model for compressing bad data and state estimation (SE), based on a revised multi-stage convex relaxation (R-Capped- L1) model. To improve the calculation efficiency, a fast decoupled solution is adopted. The proposed method can be used for three-phase unbalanced distribution networks with both phasor measurement unit and remote terminal unit measurements. The robustness and the computational efficiency of the R-Capped-L1 model with fast decoupled solution are compared with some popular SE methods by numerical tests on several three-phase distribution networks.

Abstract:This paper discusses a security-constrained integrated coordination scheduling framework for an integrated electricity-natural gas system (IEGS), in which both tight interdependence between electricity and natural gas transmission networks and their distinct dynamic characteristics at different timescales are fully considered. The proposed framework includes two linear programming models. The first one focuses on hour-based steady-state coordinated economic scheduling on power outputs of electricity generators and mass flow rates of natural gas sources while considering electricity transmission N - 1 contingencies. Using the steady-state mass flow rate solutions of gas sources as the initial value, the second one studies second-based slow gas dynamics and optimizes pressures of gas sources to ensure that inlet gas pressure of gas-fired generator is within the required pressure range at any time between two consecutive steady-state scheduling. The proposed framework is validated via an IEGS consisting of an IEEE 24-bus electricity network and a 15-node 14-pipeline natural gas network coupled by gasfired generators. Numerical results illustrate the effectiveness of the proposed framework in coordinating electricity and natural gas systems as well as achieving economic and reliable operation of IEGS.

Abstract:This paper proposes a coordinated two-stage real-time market mechanism in an unbalanced distribution system which can utilize flexibility service from home energy management system (HEMS) to alleviate line congestion, voltage violation, and substation-level power imbalance. At the grid level, the distribution system operator (DSO) computes the distribution locational marginal prices (DLMP) and its energy, loss, congestion, and voltage violation components through comprehensive sensitivity analyses. By using the DLMP components in a firststage optimization problem, the DSO generates two price signals and sends them to HEMS to seek flexibility service. In response to the request of DSO, each home-level HEMS computes a flexibility range by incorporating the prices of DSO in its own optimization problem. Due to future uncertainties, the HEMS optimization problem is modeled as an adaptive dynamic programming (ADP) to minimize the total expected cost and discomfort of the household over a forward-looking horizon. The flexibility range of each HEMS is then used by the DSO in a second-stage optimization problem to determine new optimal dispatch points which ensure the efficient, reliable, and congestionfree operation of the distribution system. Lastly, the second- stage dispatch points are used by each HEMS to constrain its maximum consumption level in a final ADP to assign consumption level of major appliances such as energy storage, heating, ventilation and air-conditioning, and water heater. The proposed method is validated on an IEEE 69-bus system with a large number of regular and HEMS-equipped homes in each phase.

Abstract:To warn the cascading failures caused by cyberattacks (CFCAs) in real time and reduce their damage on cyber-physical power systems (CPPSs), a novel early warning method based on attack gains and cost principle (AGCP) is proposed. Firstly, according to the CFCA characteristics, the leading role of attackers in the whole evolutionary process is discussed. The breaking out of a CFCA is deduced based on the AGCP from the view of attackers, and the priority order of all CFCAs is then provided. Then, the method to calculate the probability of CFCAs is proposed, and an early warning model for CFCA is designed. Finally, to verify the effectiveness of this method, a variety of CFCAs are simulated in a local CPPS model based on the IEEE 39-bus system. The experimental results demonstrate that this method can be used as a reliable assistant analysis technology to facilitate early warning of CFCAs.

Abstract:Heat storage systems with multiple heat sources play an important role in consuming extra wind power. A reasonable scheduling strategy for a hybrid system with multiple heat and electric sources could provide greater economic benefits. However, the present scheduling methods primarily focus on extra wind power consumption alone. This paper aims to develop a coordinated dispatching method that targets the maximum extra wind power consumed and highest economic benefit of the hybrid energy system as the optimization objective. A two-step coordinated dispatching method is proposed, where the first step focuses on optimizing the extra wind power consumed by coordinating the consumption quota for different types of energy sources at the system level and distributes the consumption share for every unit within each type of energy source, thereby maximizing fuel savings and economic benefits in the second step. The effectiveness of the approach is demonstrated using simulation results for an electric-heat hybrid system. Compared with two existing dispatching methods, the scheduling strategy presented in this paper could consume more extra wind power and provide higher fuel savings and economic benefits.

Abstract:Due to the growing penetration of renewable energies (REs) in integrated energy system (IES), it is imperative to assess and reduce the negative impacts caused by the uncertain REs. In this paper, an unscented transformation-based mean-standard (UT-MS) deviation model is proposed for the stochastic optimization of cost-risk for IES operation considering wind and solar power correlated. The unscented transformation (UT) sampling method is adopted to characterize the uncertainties of wind and solar power considering the correlated relationship between them. Based on the UT, a mean-standard (MS) deviation model is formulated to depict the trade-off between the cost and risk of stochastic optimization for the IES optimal operation problem. Then the UT-MS model is tackled by a multi-objective group search optimizer with adaptive covariance and Le′vy flights embedded with a multiple constraints handling technique (MGSO-ACL-CHT) to ensure the feasibility of Peratooptimal solutions. Furthermore, a decision making method, improve entropy weight (IEW), is developed to select a final operation point from the set of Perato-optimal solutions. In order to verify the feasibility and efficiency of the proposed UT-MS model in dealing with the uncertainties of correlative wind and solar power, simulation studies are conducted on a test IES. Simulation results show that the UT-MS model is capable of handling the uncertainties of correlative wind and solar power within much less samples and less computational burden. Moreover, the MGSOACL- CHT and IEW are also demonstrated to be effective in solving the multi-objective UT-MS model of the IES optimal operation problem.

Abstract:Increasing energy consumption has caused power systems to operate close to the limit of their capacity. The distributed power flow controller (DPFC), as a new member of distributed flexible AC transmission systems, is introduced to remove this barrier. This paper proposes an optimal DPFC configuration method to enhance system loadability considering economic performance based on mixed integer linear programming. The conflicting behavior of system loadability and DPFC investment is analyzed and optimal solutions are calculated. Thereafter, the fuzzy decision-making method is implemented for determining the most preferred solution. In the most preferred solution obtained, the investment of DPFCs is minimized to find the optimal number, locations and set points. Simulation results on the IEEERTS79 system demonstrate that the proposed method is effective and reasonable.

Abstract:A fractional frequency transmission system (FFTS) is the most competitive choice for long distance transmission of offshore wind power, while the Hexverter, as a newly proposed direct AC/AC converter, is an attractive choice for its power conversion. This paper proposes a novel control scheme characterizing the global stability and strong robustness of the Hexverter in FFTS applications, which are based on the interconnection and damping assignment passivity-based control (IDA-PBC) methodology. Firstly, the frequency decoupled model of the Hexverter is studied and then a port-controlled Hamiltonian (PCH) model is built. On this basis, the IDAPB control scheme of the Hexverter is designed. Considering the interference of system parameters and unmodeled dynamics, integrators are added to the IDA-PB controller to eliminate the steady-state error. In addition, the voltagebalancing control is applied in order to balance the capacitor DC voltages to obtain a better performance. Finally, the simulation results and experimental results are presented to verify the effectiveness and superiority of the IDA-PB controller.

Abstract:This paper proposes a new method to allocate the transmission fixed costs among the network participants in a pool-based electricity market. The allocation process relies on the circuit laws, utilizes the modified impedance matrix and is performed in two individual steps for the generators and loads. To determine the partial branch power flows due to the participants, the equal sharing principle is used and validated by the Shapley and Aumann-Shapley values as two preferred game-theoretic solutions. The proposed approach is also applied to determine the generators’ contributions into the loads, and a new concept, named circuit-theory-based equivalent bilateral exchange (EBE), is introduced. Using the proposed method, fairly stable tariffs are provided for the participants. Cross-subsidies are reduced and a fair competition is made by the proposed method due to the counter-flows being alleviated compared with the well-known Z-bus method. Numerical results are reported and discussed to validate the proposed cost allocation method. Comparative analysis reveals that the method satisfies all conditions desired in a fair and efficient cost allocation method. Finally, the developed technique has been implemented successfully on the 2383-bus Polish power system to emphasize that the method is applicable to very large systems.

Abstract:The wide utilization of gas-fired generation and the rapid development of power-to-gas technologies have led to the intensified integration of electricity and gas systems. The random failures of components in either electricity or gas system may have a considerable impact on the reliabilities of both systems. Therefore, it is necessary to evaluate the reliabilities of electricity and gas systems considering their integration. In this paper, a novel reliability evaluation method for integrated electricity–gas systems (IEGSs) is proposed. First, reliability network equivalents are utilized to represent reliability models of gas-fired generating units, gas sources (GSs), power-to-gas facilities, and other conventional generating units in IEGS. A contingency management schema is then developed considering the coupling between electricity and gas systems based on an optimal power flow technique. Finally, the time-sequential Monte Carlo simulation approach is used to model the chronological characteristics of the corresponding reliability network equivalents. The proposed method is capable to evaluate customers’ reliabilities in IEGS, which is illustrated on an integrated IEEE Reliability Test System and Belgium gas transmission system.

Abstract:Smart grid has integrated an increasing number of distributed energy resources to improve the efficiency and flexibility of power generation and consumption as well as the resilience of the power grid. The energy consumers on the power grid, e.g., households, equipped with distributed energy resources can be considered as ‘‘microgrids’’ that both generate and consume electricity. In this paper, we study the energy community discovery problems which identify energy communities for the microgrids to facilitate energy management, e.g., load balancing, energy sharing and trading on the grid. Specifically, we present efficient algorithms to discover such communities of microgrids considering both their geo-locations and net energy (NE) over any period. Finally, we experimentally validate the performance of the algorithms using both synthetic and real datasets.

Abstract:Better flexibility and controllability have been introduced into distribution system with the development of new loads and resources. As a consequence, the connotations and tools for evaluating the planning solution need to be further enriched. This paper proposes a fast algorithm to quantify steady-state voltages and load profiles in distribution system by simulating the manipulation control process of controllable resources, taking the efficiency and ease of use into account. In this method, a complex distribution system is decoupled into several simple parts according to the ports of the DC interlink. Then, to achieve the qualified voltages and load profiles, the manipulation details of controllable resources are simulated following a certain control sequence in each part. Finally, the analysis results of each part are matched and filtered to obtain a complete evaluation. Five of the most commonly controllable resources are considered in this method. The effectiveness of the proposed method is demonstrated through a case study based on field data from an actual distribution system.

Abstract:In modern distribution system, the distribution system operator (DSO) acts as a market facilitator and data manager as well as an energy supplier and operation controller. In this circumstance, the DSO should comprehensively consider the diversified participants of the modern distribution system when making investment decisions of distributed generation (DG). This paper proposes a DG planning model considering the behavior of the diversified participants, which are motivated to cooperate with distributed renewable energy resources to promote their integration, and to achieve the optimal DG investment plan. The optimization model takes a centralized structure but fully considers the preferences, profits and comfort levels of the aggregators and consumers. The model is linearized into a mixed-integer linear programming (MILP) problem and is solved by CPLEX. Results of the case study show that when the DSO spares subsidies to the aggregators and consumers to encourage their participation in demand response (DR) programs, it earns more compared with providing no subsidies for DR participation. It is also demonstrated that the overall profit increases as the subsidies increase within a certain range, but decreases when the subsidies exceed this range. Therefore, the DSO needs to carefully choose the subsidization level to achieve the optimal utilization of renewable energy and demand flexibility. The optimal subsidization level is derived from the model proposed in this paper. Therefore, this paper puts forward a new pattern to utilize the distributed renewable energy sources, and provides guidance in policy making and DR program implementation.

Abstract:A hierarchical approach for the energy management of geographically close microgrids connected through a dedicated AC power network is proposed in this paper. The proposed approach consists of a two-layer energy management system (EMS) for networked microgrids. In the lower layer, each microgrid solves its own economic dispatch problem through a distributed model predictive control approach that respects capacity limits and ramp-rate constraints of distributed generation. In the upper layer, the energy trading in the network of microgrids decides how to optimally trade the energy based on the marginal cost information from the lower layer in order to improve global optimization objectives, e.g., social welfare. In order to solve the trading problem, a consensusbased algorithm and a replicator dynamics algorithm are proposed assuming that the marginal cost function of the microgrid is known and linear. It is shown that both algorithms converge to the same solution, which is equivalent to the minimization of operation costs. The consensus-based algorithm is extended in order to tackle more general marginal cost functions and trading network constraints. Moreover, the effect of ramp constraints and network limits is studied. Simulations show the effectiveness of the proposed algorithms for three interconnected microgrids with different characteristics.

Abstract:To ensure the safety and reliability of the distribution network and adapt to the uncertain development of renewable energy sources and loads, a two-stage distributionally robust optimization model is proposed for the active distribution network (ADN) optimization problem considering the uncertainties of the source and load in this paper. By establishing an ambiguity set to capture the uncertainties of the photovoltaic (PV) power, wind power and load, the piecewise-linear function and auxiliary parameters are introduced to help characterize the probability distribution of uncertain variables. The optimization goal of the model is to minimize the total expected cost under the worst-case distribution in the ambiguity set. The first-stage expected cost is obtained based on the predicted value of the uncertainty variable. The second-stage expected cost is based on the actual value of the uncertainty variable to solve the first-stage decision. The generalized linear decision rule approximates the two-stage optimization model, and the affine function is introduced to provide a closer approximation to the second-stage optimization model. Finally, the improved IEEE 33-node and IEEE 118-node systems are simulated and analyzed with deterministic methods, stochastic programming, and robust optimization methods to verify the feasibility and superiority of the proposed model and algorithm.

Abstract:Distributed generations (DGs) are main components for active distribution networks (ADNs). Owing to the large number of DGs integrated into distribution levels, it will be essential to schedule active and reactive power resources in ADNs. Generally, energy and reactive power scheduling problems are separately managed in ADNs. However, the separate scheduling cannot attain a global optimum scheme in the operation of ADNs. In this paper, a probabilistic simultaneous active/reactive scheduling framework is presented for ADNs. In order to handle the uncertainties of power generations of renewable-based DGs and upstream grid prices in an efficient framework, a stochastic programming technique is proposed. The stochastic programming can help distribution system operators (DSOs) to make operation decisions in front of existing uncertainties. The proposed coordinated model considers the minimization of the energy and reactive power costs of all distributed resources along with the upstream grid. Meanwhile, a new payment index as loss profit value for DG units is introduced and embedded in the model. Numerical results based on the 22-bus and IEEE 33-bus ADNs validate the effectiveness of the proposed method. The obtained results verify that through the proposed stochastic-based power management system, the DSO can effectively schedule all DGs along with its economic targets while considering severe uncertainties.

Abstract:As an emerging paradigm in distributed power systems, microgrids provide promising solutions to local renewable energy generation and load demand satisfaction. However, the intermittency of renewables and temporal uncertainty in electrical load create great challenges to energy scheduling, especially for small-scale microgrids. Instead of deploying stochastic models to cope with such challenges, this paper presents a retroactive approach to real-time energy scheduling, which is prediction-independent and computationally efficient. Extensive case studies were conducted using 3-year-long real-life system data, and the results of simulations show that the cost difference between the proposed retroactive approach and perfect dispatch is less than 11% on average, which suggests better performance than model predictive control with the cost difference at 30% compared to the perfect dispatch.

Abstract:In power market environment, the growing importance of demand response (DR) and renewable energy source (RES) attracts more for-profit DR and RES aggregators to compete with each other to maximize their profit. Meanwhile, the intermittent natures of these alternative sources along with the competition add to the probable financial risk of the aggregators. The objective of the paper is to highlight this financial risk of aggregators in such uncertain environment while estimating DR magnitude and power generated by RES. This work develops DR modeling incorporating the effect of estimating power at different confidence levels and uncertain participation of customers. In this paper, two well-known risk assessment techniques, value at risk and conditional value at risk, are applied to predict the power from RES and DR programs at a particular level of risk in different scenarios generated by Monte Carlo method. To establish the linkage between financial risk taking ability of individuals, the aggregators are classified into risk neutral aggregator, risk averse aggregator and risk taking aggregator. The paper uses data from Indian Energy Exchange to produce realistic results and refers certain policies of Indian Energy Exchange to frame mathematical expressions for benefit function considering uncertainties for each type of three aggregators. Extensive results show the importance of assessing the risks involved with two unpredictable variables and possible impacts on technical and financial attributes of the microgrid energy market.

Abstract:This paper introduces a bilevel programming approach to electricity tariff optimization for the purpose of demand response management (DRM) in smart grids. In the multi-follower Stackelberg game model, the leader is the profit-maximizing electricity retailer, who must set a time-of-use variable energy tariff in the grid. Followers correspond to groups of prosumers (simultaneous producers and consumers of the electricity. They response to the observed tariff, schedule controllable loads and determine the charging/discharging policy of their batteries to minimize the cost of electricity and to maximize the utility at the same time. A bilevel programming formulation of the problem is defined, and its fundamental properties are proven. The primal-dual reformulation is proposed in this paper to convert the bilevel optimization problem into a single-level quadratically constrained quadratic program (QCQP), and a successive linear programming (SLP) algorithm is applied to solve it. It is demonstrated in computational experiments that the proposed approach outperforms typical earlier methods based on the Karush– Kuhn–Tucker (KKT) reformulation regarding both solution quality and computational efficiency on practically relevant problem sizes. Besides, it also offers more flexible modeling capabilities.

Abstract:The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers. To address this issue, an incentive-based demand response (DR) model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers. The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions. The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price. Then, the sensitivity of incentive price on retailers’ benefits is analyzed and the optimal incentive price is obtained according to the DR model. The case study verifies the effectiveness of the proposed model.

Abstract:Fast and accurate forecasting of schedulable capacity of electric vehicles (EVs) plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems. Traditional methods are insufficient to deal with large-scale actual schedulable capacity data. This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales. The time scale of these data analysis comprises the real time of one minute, ultra-short-term of one hour and one-day-ahead scale of 24 hours. The predicted results for different time scales can be used for various ancillary services. The proposed algorithm is validated using operation data of 521 EVs in the field. The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm, the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.

Abstract:This paper proposes a robust controller to improve power system stability and mitigate subsynchronous interaction (SSI) between doubly-fed induction generator (DFIG)-based wind farms and series compensated transmission lines. A robust stability analysis is first carried out to show the impact of uncertainties on the SSI phenomenon. The uncertainties are mainly due to the changes in the power system impedance (e.g., transmission line outages) and the variations of wind farm operating conditions. Then, using the l-synthesis technique, a robust SSI damping controller is designed and augmented to the DFIG control system to effectively damp the SSI oscillations. The output signals of the supplementary controller are dynamically limited to avoid saturating the converters and to provide DFIG with the desired fault-ride-through (FRT) operation during power system faults. The proposed controller is designed for a realistic test system with multiple series capacitor compensated lines. The frequency of the unstable SSI mode varies over a wide range due to the changes in power system topologies and wind farm operating conditions. The performance of the proposed controller is verified through electromagnetic transient (EMT) simulations using a detailed wind farm model. Simulation results also confirm the grid compliant operation of the DFIG.

Abstract:Due to the uncertainty of the accuracy of wind power forecasting, wind turbines cannot be accurately equated with dispatchable units in the preparation of a day-ahead dispatching plan for power grid. A robust optimization model for the uncertainty of wind power forecasting with a given confidence level is established. Based on the forecasting value of wind power and the divergence function of forecasting error, a robust evaluation method for the availability of wind power forecasting during given load peaks is established. A simulation example is established based on a power system in Northeast China and an IEEE 39-node model. The availability estimation parameters are used to calculate the equivalent value of wind power of the conventional unit to participate in the day-ahead dispatching plan. The simulation results show that the model can effectively handle the challenge of uncertainty of wind power forecasting, and enhance the consumption of wind power for the power system.

Abstract:The three-phase dual active bridge (3p-DAB) converter is widely considered in next-generation DC grid applications. As for traditional AC grids, the successful integration of power electronic converters in DC grids requires accurate time-domain system-level studies. As demonstrated in the existing literature, the development and efficient implementation of large-signal models of 3p-DAB converters are not trivial. In this paper, a generalized average model is developed, which enables system-level simulation of DC grids with 3p-DAB converters in electromagnetic transient type (EMT-type) programs. The proposed model is rigorously compared with alternative modeling techniques: ideal-model, switching-function and state-space averaging. It is concluded that the generalized average model provides an optimal solution when accuracy of transient response, reduction in computation time, and wideband response factors are considered.

Abstract:To explore the clustered voltage balancing mechanism of the cascaded H-bridge static synchronous compensator (STATCOM), this paper analyzes the causes of unbalanced clustered voltage. The negative-sequence current caused by the compensation of unbalanced reactive power or detection and control errors and the zero-sequence voltage caused by voltage drift of the STATCOM neutral point contribute to unbalanced clustered voltage. On this basis, this paper proposes a control strategy to inject negative-sequence current and zero-sequence voltage simultaneously. The injection of negative-sequence current may cause current asymmetry in the grid, and the zero-sequence injection has a relatively limited balancing ability in the clustered voltages. The proposed control strategy can not only generate a faster balancing response than the traditional zero-sequence voltage injection method, but also lower the extent of current asymmetry compared with the traditional negative-sequence current injection method. Then, the negative-sequence current and zero-sequence voltage injection are further transformed into the dq frame to establish a unified frame. The effectiveness of the proposed control strategy is verified by the simulation and experimental results.

Abstract:Market power is known as the ability of units and generation companies (GenCos) to change electricity price profitably. As cleared in the definition, locational marginal price (LMP) is the most important key in market power evaluation. Therefore, the main objective of the paper is to analyze market power of units and GenCos based on their abilities to change electricity price. At the first step, using Karush-Kuhn-Tucker (KKT) conditions of Lagrangian method, LMP is decomposed into four main components. These components indicate the share of each unit at the LMP of each bus. These values are calculated by the proposed analytical method, and cannot be obtained using simulation methods. At the second step, “unit-based LMP_S” index, which indicates the contribution factor of each unit at LMP of each bus, is proposed as a new structural market power index. This index is also used as an effective tool to determine the most profitable coalition between two units. Using that, the market operator can predict highly potential collusions. Moreover, “GenCos-based LMP_S” index is proposed. Using this effective tool, the contribution of each GenCo, which owns multiple units in various buses, at the LMP of each bus is discovered. The proposed market power indices are calculated on the IEEE 24-bus test system and compared with some conventional structural market power indices. Incremental profits of units due to change of unit’s strategies verify the accuracy of proposed method.

Abstract:With the large-scale integration of renewable generation, energy storage system (ESS) is increasingly regarded as a promising technology to provide sufficient flexibility for the safe and stable operation of power systems under uncertainty. This paper focuses on grid-scale ESS planning problems in transmission-constrained power systems considering uncertainties of wind power and load. A scenario-based chance-constrained ESS planning approach is proposed to address the joint planning of multiple technologies of ESS. Specifically, the chance constraints on wind curtailment are designed to ensure a certain level of wind power utilization for each wind farm in planning decision-making. Then, an easy-to-implement variant of Benders decomposition (BD) algorithm is developed to solve the resulting mixed integer nonlinear programming problem. Our case studies on an IEEE test system indicate that the proposed approach can co-optimize multiple types of ESSs and provide flexible planning schemes to achieve the economic utilization of wind power. In addition, the proposed BD algorithm can improve the computational efficiency in solving this kind of chance-constrained problems.

Abstract:With the construction of the “3-level, 3-vertical-line, and 1-circle” power backbone in China, it’s stricter and stricter on relay protection system and security and stability control system (SSCS) for reliable power transmission. Lots of blackouts in the world had happened, one main reason for which is the hidden failures of relay protection system or SSCS. Much work had been done about the hidden failure of relay protection, including classification, probability model, analysis methods of effects on power grid, and monitoring measures, which was summarized in the paper. The operation experiences of SSCS indicated that there might be hidden failures in five links of the security and stability control device (SSCD), e.g. measuring, control strategy, setting, communication and voting pattern. In addition, the coordination hidden failure among relay protection system, SSCS, and power plant’s parameters related to the power grid was pointed out for more attention. In the future, amounts of work will be expected to be conducted on hidden failure: model building, assessment methods, application of research achievements, operation management of secondary equipment, and coordination problem between the relay protection system and the SSCS.

Abstract:Due to the popularization of distributed energy resources (DERs), the aggregated prosumer effect excels a general energy storage system characteristic. Virtual energy storage system (VESS) concept is proposed hereby that mimics an actual storage unit and incorporates the same charging (consumer) and discharging (producer) modes. It is possible to provide ancillary services via VESS by exploiting the flexibility and thus much research has been proposed on the optimization of the VESS scheduling. In general, the charging and discharging efficiencies of VESS are different and there can be only one status at a time slot. To achieve the optimal schedule while considering the constraints above, binary terms should be introduced into the optimization problem which end up with a nonconvex problem. In this paper, a complimentary mathematical proof is given for the convexification of this mixed integer linear programming (MILP) problem so that the linear programming (LP) method can be applied instead if the objective function is linear. The proposed proof is validated through a case study and the simulation results show the effectiveness of the proposed method.