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Distribution-level electricity market provides a platform for trading energy and grid services from large-amount of small-scale distributed energy resources (DERs) located on distribution grids. The behavior of the DERs in the distribution electricity market may ultimately impact the market-clearing and locational marginal prices (LMPs) in the wholesale market. This paper proposes a bi-level optimization model for distribution market clearing and distribution locational marginal pricing (DLMP) considering the interactions between distribution and transmission wholesale markets. In the proposed model, the upper-level model represents the distribution system operator (DSO) market-clearing and the lower-level model represents the wholesale market-clearing by the independent system operator (ISO). The LMP at the substation will impact the DER dispatch and power demands of the DSO as well as the DLMP. In turn, the power demands of the DSO will further impact the ISO market and its LMPs. The equilibrium problem with equilibrium constraints (EPEC) approach is applied to find the equilibria of multiple DSOs and the ISO. The EPEC problem is transformed into a single-level mixed-integer convex problem in order to allow for efficient solving. The effectiveness of the proposed model and solution method are demonstrated through case studies.

In this paper, a regret-based risk-averse stochastic production task and energy management (PTEM) model for industrial microgrid is proposed, which is applied to a battery manufacturing plant (BMP) microgrid. We incorporate production task scheduling with production constraints in the energy management of industrial microgrid. The general PTEM of industrial microgrids is analyzed and the production processes and energy requirements of a BMP multi-energy microgrid prosumer are presented, in which thermostatically controlled loads (TCLs) in buildings are considered. Then, the regret-based risk-averse PTEM model is formulated as a mixed-integer linear programming (MILP) problem based on stochastic programming considering uncertainties of day-ahead electricity market price and photovoltaic (PV) power output. Conditional value-at-risk (CVaR) is introduced for risk control and utilized for minimizing the expected regret. Numerical studies are carried out to validate the proposed model by comparing with risk-neutral approach and deterministic model based on real market prices from PJM electricity market. The results show that the proposed PTEM approach is effectively risk-averse. In addition, the impacts of total target production task and risk-control parameter on expected cost and regret are analyzed.

Power-to-gas (P2G) and gas-fired generation units (GFGUs) enable the bidirectional energy flow of integrated energy systems (IESs). The static security of IES should be analysed considering the interdependence between energy systems. In this study, the static security of IES is analysed, and a novel static security control strategy to improve the security of IES after N−1 contingency is proposed considering P2G. First, electricity–gas IES model is presented, and the multi-energy flow of IES is solved by the Newton–Raphson method. Then, N − 1 static security analysis is carried out for the electricity system and natural gas system. Based on the obtained Jacobian matrix during the energy flow calculation process after N − 1 contingency, variable sensitivity matrixes of voltage, gas pressure, branch power flow and pipeline flow to control variables are derived. Furthermore, a static security control strategy for IES based on the sensitivity matrices is developed to regulate operation statuses of P2G and GFGU and compressor outlet pressure to mitigate the violation of security constraints. The performance of the proposed analysis method and control strategy is evaluated by IES 4-12 and IES 118-48 test systems. The results demonstrate that the proposed control strategy can mitigate security risks of IES after N − 1 contingencies.

Aiming to achieve the flexible operation of distribution network (ADN), an energy management strategy of ADN with integrated distributed wind power and smart buildings is proposed in this study. First, based on the thermal storage characteristics of buildings, the resistor–capacitor model is used to develop an energy consumption prediction model of smart buildings. Second, the smart buildings are modelled as flexible resources of ADN. A multi-objective ADN model is further formulated based on analytic hierarchy process (AHP) by comprehensively considering constraints of power grid and smart buildings. Then, the unified model of ADN is transformed by second-order conic relaxation to guarantee the global optimal solution. Finally, the scheduling results for the unified model of ADN under different control methods for heating ventilation and air-conditioning (HVAC) systems are compared in the winter heating scenario. In addition, the impact of the demand response capability for smart buildings on the economic and secure operation of the ADN is further analysed. Numerical studies demonstrate that the proposed method can increase the penetration of local wind power, reduce peak-valley load difference and network loss of the grid while ensuring the comfort temperature of customers, and further achieve the flexible operation of ADN.

This paper proposes an adjustable robust power dispatch (ARPD) model under low-carbon economy to accommodate the uncertainty of wind power with combined wind-storage system (CWSS) and carbon capture power plants (CCPPs). First, battery energy storage (BES), which is considered as adjustable under worst-case scenarios, is introduced to wind power plants to form a combined wind storage system structure to improve operation flexibility. Then, a dispatching model of CCPPs is presented and analysed. The operating characteristics of CCPPs make it possible to control net power outputs and carbon emissions independently and flexibly. The integration of BES and carbon capture and storage (CCS) improves the operation flexibility of both wind power plants and conventional power plants. The objective function of the proposed model under interval-uncertain wind power is to minimize the total cost under the worst wind power output case and the context of low-carbon economy. The proposed model is implemented on the modified PJM 5-bus system with one wind farm and the IEEE-118 bus system with five wind farms. Numerical results demonstrate that the proposed model is effective at reducing carbon emissions and wind power curtailment, and verify that as the number of CCPPs increases, the overall costs come down.

Power-to-gas (P2G) facilities and natural gas fired power units provide flexibility to integrated electricity and natural gas systems (IENGS) for wind power accommodation and ramp deployment. This paper proposes a stochastic coordinated scheduling model for IENGS considering ramping costs with P2G storage and wind power. The operation model of natural gas system with P2G is presented, and the benefits of P2G integration are analyzed. To address the uncertainties of wind power and energy loads, multiple representative scenarios are generated. The flexible ramping requirements and costs are incorporated and analyzed, and flexible ramp can be provided by P2G in this work. The coordinated scheduling model for IENGS is formulated as a two-stage stochastic programming problem, in which day-ahead scheduling for electricity systems is modeled in the first stage model and scheduling of natural gas systems is carried out in the second stage model. Numerical case studies on a modified PJM 5-bus electricity system with a 7-node natural gas system and the IEEE 118-bus system with a 20-node Belgian natural gas system verify that P2G can help accommodate wind power, provide additional flexible ramping capacities, and reduce the gas supply from gas suppliers and gas load shedding.

In traditional integrated electricity and district heating systems, the inflexible operation of combined heat and power units leads to a large amount of wind power curtailments during winter. The thermal inertia of aggregated buildings can provide additional operational flexibility to promote wind power accommodation. In this study, a day-ahead scheduling model for integrated electricity and district heating system considering the thermal inertia of buildings is proposed. In this work, the operation model of the district heating network under constant mass flow and variable temperature operation strategy is presented, and the aggregated model of buildings based on the detailed thermal model of buildings is established. Then, the scheduling framework is analysed and the day-ahead scheduling model is formulated as quadratic programming problem to minimise the operation cost of integrated electricity and district heating system. The validity of the proposed model is verified by the case studies performed on a 6-bus power system with a 6-node heating system and IEEE 39-bus electricity system with a 16-node heating system. The results demonstrate that the thermal inertia of buildings can provide additional operational flexibility and effectively help reduce wind power curtailment and operation costs.

Aiming to utilize the flexibility of smart buildings for flexible operation of active distribution network, a combined modeling and optimal scheduling method for the active distribution network with integrated smart buildings is proposed in this paper. Based on the heat storage characteristics of a building, the energy consumption prediction model of the building considering different heating zones with different orientations is developed using the Resistor-Capacitor thermal network model. Then, different optimal control methods of the heating, ventilation and air-conditioning system in the building are developed. The energy consumption management of the heating, ventilation and air-conditioning system is achieved by adjusting the room temperature within the suitable temperature comfort range. In order to further consider the impact of the integration of smart buildings on the economic and security operation of the active distribution network, the optimal scheduling method of the active distribution network with integrated smart buildings is developed considering the load factor of the aggregation of the smart buildings. Finally, the optimal scheduling results of the aggregation of the smart buildings under different heating, ventilation and air-conditioning control methods in the winter heating scenario are analyzed. In addition, based on the branch flow model, the optimal power flow model of active distribution network with on-load tap changer is constructed by piecewise linearization and second-order cone relaxation to achieve flexible and optimal operation of the active distribution network. Thus, the impacts of the optimal schedules of the aggregation of smart buildings on the economic and security operation of the active distribution network are further evaluated. Numerical studies demonstrate that the proposed optimal scheduling method can make full use of the demand response potential of the smart buildings and further contribute to the operating costs reduction of the smart buildings. Meanwhile, the optimization of the active distribution network with the load factor of the aggregation of buildings can reduce the power loss and increase the minimum voltage magnitude of the active distribution network utilizing the flexibility of the smart buildings.

The integration of large-scale wind power brings challenges to the operation of integrated energy systems (IES). In this paper, a day-ahead scheduling model for IES with wind power and multi-type energy storage is proposed in a scenario-based stochastic programming framework. The structure of the IES consists of electricity, natural gas, and heating networks which are all included in the model. Operational constraints for IES incorporating multi-type energy storage devices are also considered. The constraints of the electricity network, natural gas network and heating network are formulated, and nonlinear constraints are linearized. The calculation method for the correlation of wind speed between wind farms based on historical data is proposed. Uncertainties of correlated wind power were represented by creating multiple representative scenarios with different probabilities, and this was done using the Latin hypercube sampling (LHS) method. The stochastic scheduling model is formulated as a mixed integer linear programming (MILP) problem with the objective function of minimizing the total expected operation cost. Numerical results on a modified PJM 5-bus electricity system with a seven-node natural gas system and a six-node heating system validate the proposed model. The results demonstrate that multi-type energy storage devices can help reduce wind power curtailments and improve the operational flexibility of IES.

Green energy is driving the evolutions of energy industry and carbon emission is becoming an important concern. Considering the increasing couplings among various energy sectors, this paper investigates multi-period optimal energy flow and energy pricing in carbon-emission embedded integrated energy systems, including electricity, natural gas, and district heating networks. Firstly, an optimal scheduling model of integrated energy systems was proposed in this paper. The models of DC power flow, natural gas pipeline flow and heating network energy flow are presented and linearized for the optimization problem. Natural gas-fired generators and combined heat and power (CHP) units are modeled as coupling components of electricity-gas and electricity-heating networks. Then, based on the optimal scheduling model, the locational marginal prices (LMP) for electricity, natural gas and heating network are determined. Moreover, the carbon emission caused by energy production has been taken into account in the optimal scheduling and energy pricing process. Finally, case studies on a combined network consisting of IEEE 39-bus system, Belgium 20-node natural gas system and 6-node heating system demonstrate the effectiveness of the proposed model and the impacts of carbon emission on system scheduling and LMP.

The integration of high-penetration distributed generators (DGs) with smart inverters and the emerging power electronics technology of soft open points provide increased controllability and flexibility to the operation of active distribution networks. Existing works on distributed energy storage planning have not fully considered the coordinated operation of these new power electronic devices with distributed energy storage systems, leading to less economic investment decisions. This paper proposes an optimal planning model of distributed energy storage systems in active distribution networks incorporating soft open points and reactive power capability of DGs. The reactive power capability of DG inverters and on load tap changers are considered in the Volt/VAR control. Moreover, soft open points are modeled to provide flexible active and reactive power control on the associated feeders. Hourly network reconfiguration is conducted to optimize the power flow by changing the network topology. A mixed-integer second-order cone programming model is formulated to optimally determine the locations and energy/power capacities of distributed energy storage systems. Finally, the effectiveness of the proposed model is validated on a modified IEEE 33-node distribution network. Considering soft open points, DG reactive power capability, and network reconfiguration, the results demonstrate the optimal distributed energy storage systems planning obtained by the proposed model achieves better economic solution.

Global warming caused by excessive CO2 emissions has make it urgent to develop low-carbon economy. The carbon capture system is effective to reduce the carbon footprint of coal-fired power plants. Meanwhile, using more renewable energies such as wind power will require less generations from traditional power plants and hence limit the overall carbon emission. However, the wind power is intermittent by nature and hence may fluctuate and present a stochastic feature. In order to effectively reduce the overall carbon emission, a stochastic day-ahead scheduling optimization model with wind power integration incorporating carbon capture power plant (CCPP) and coupon-based demand response (CDR) is proposed in this work. Firstly, the formulation of CDR and the operating mechanism of CCPPs are clarified. Then the flexible operation mechanism aiming at reducing wind power curtailment and CO2 emissions is analyzed. The random forecasting errors of the day-ahead hourly wind power output and loads are considered. Monte Carlo method is applied to simulate stochastic scenarios and a scenario reduction method is applied to ease the computational burden. Simulation results with on the PJM 5-bus system and the IEEE 118-bus system demonstrate the effectiveness of the proposed method in carbon emission reduction and wind curtailments decrease.

Aiming to further achieve the flexible operation of the active distribution network by utilizing heating, ventilation and air-conditioning systems and electric vehicles of office buildings, a hierarchical scheduling method of active distribution network considering flexible loads in office buildings is proposed in this paper. In stage I, based on the heat storage property of office buildings and the behavioral property of office workers, an energy optimal scheduling model of the office building incorporating heating, ventilation and air-conditioning systems and electric vehicles is established. In stage II, an optimal scheduling method of the active distribution network considering on-load tap changer and flexible loads (i.e., heating, ventilation and air-conditioning systems and electric vehicles) in office buildings is developed by using the second-order conic relaxation and piecewise linearization. Finally, the scheduling results for the heating, ventilation and air-conditioning systems and electric vehicles in office buildings under different cases are compared in a winter scenario, and the security and economy of the active distribution network considering flexible loads in office buildings are further analyzed. Numerical studies demonstrate that the proposed method can contribute to the office building cost reduction and the load factor increment while guaranteeing the heating and the transportation demands of office workers. It can further improve the security and economic operation of the distribution network.

Evaluation of available transfer capability (ATC) is a complicated process involving the determination of the total transfer capability (TTC) and the existing transfer commitments (ETC). Considering the uncertain renewable generation such as wind power will bring more challenge to this process. Previously the uncertainty of wind power is considered either in the ED model or the following TTC calculation, but not included in two process simultaneously. Therefore, the ATC output may not be accurate since the uncertainty impact two problem simultaneously. To consider the uncertainty and correlation of wind power in both ISO’s ED and ATC evaluation, this paper proposes a bi-level optimization framework in which the ATC evaluation is formulated as the upper level problem and the ED is the lower level. The bi-level model is first converted to a mathematic program with equilibrium constraints (MPEC) by recasting the lower level problem as its Karush-Kuhn-Tucker (KKT) optimality conditions, and then transformed to a mixed-integer linear programming (MILP) problem which can be solved by existing optimization tools. Case studies on the PJM 5-bus and IEEE 118-bus systems are presented to verify the proposed methodology and the impacts of correlation of wind power on ATC are analysed.

This work proposes a measurement-based voltage stability index, namely wide-area measurement-based voltage stability sensitivity based on L index. The sensitivities of L index with respect to nodal reactive power (Q) and real power (P) injections are first derived. The derived L-Q and L-P sensitivities analyze the impact of nodal injection to nodal voltage stability and can help determine the reactive power compensation and emergency load shedding amount for voltage stability control. To improve the computational efficiency, a simplified L index, L′, along with its sensitivities with respect to nodal reactive and real power injection (L′-Q and L′-P sensitivities) are derived which makes the proposed approach suitable for the practical large-scale systems. Moreover, a control strategy for voltage stability is proposed based on the L-Q, L-P, L′-Q, and L′-P sensitivities. The proposed sensitivities and control strategy are tested on the New England 39 bus system and the IEEE 118 system. Test results on both systems verify the proposed sensitivities and the control strategy by demonstrating their accuracy and robustness in voltage stability assessment and control. In conclusion, the proposed measurement-based sensitivities can be applied to voltage stability assessment and control by using the wide-area measurements.

Worldwide natural gas consumption has increased significantly, especially for power generation in electricity systems with the gas-to-power (G2P) process of natural gas fired units. Supply for both natural gas and electricity systems should be dispatched economically and simultaneously due to their firm interconnection. This paper proposes a security-constrained bi-level economic dispatch (ED) model for integrated natural gas and electricity systems considering wind power and power-to-gas (P2G) process. The upper level is formulated as an ED optimization model for electricity system, while the lower level is an optimal allocation problem for natural gas system. Natural gas system is modeled in detail. In addition, the security constraints and coupling constraints for the integrated energy systems are considered. The objective function is to minimize the total production cost of electricity and natural gas. The lower model is converted and added into the upper model as Karush-Kuhn-Tucher (KKT) optimality conditions, thus the bi-level optimization model is transformed into a mix-integer linear programming (MILP) formulation. Numerical case studies on the PJM-5bus system integrated with a seven-node gas system and IEEE 118-bus system integrated with a modified Belgian high-calorific gas system demonstrate the effectiveness of the proposed bi-level ED model.

With the increasing installed capacity of wind power and the interdependencies among multiple energy sectors, optimal operation of integrated energy systems (IES) with combined cooling, heating and power (CCHP) is becoming more important. This paper proposes an optimal dispatch strategy for IES with CCHP and wind power. Natural gas system is modeled and its security constraints are integrated into the optimal dispatch model. The gas shift factor (GSFgas) matrix for natural gas system is derived to quantify the impact of gas supply and load at each node on the gas flow through the pipelines so that the pipeline flow equation is linearized. The objective function of the optimization model is to minimize the total operation cost of IES. Then the model is transformed into mixed integer linear programming (MILP) formulation to improve the computation efficiency. Numerical case studies conducted demonstrate the lower operation cost of the proposed model facilitating wind power integration.

The increasing installment of distributed generations (DGs) and the tight coupling between active distribution system (ADS) and district heating system (DHS) enhance the flexibility of electricity-heat integrated energy system (IES) to participate in the operation of power transmission system. The operation of transmission system will be affected by distribution company (DISCO). In this paper, a collaborative optimal operation model of transmission system is proposed considering the interaction with the district electricity-heat integrated energy systems based on a semi-definite programming relaxation method. The optimal operation problem of ADS is modeled based on AC optimal power flow (ACOPF) with second order cone (SOC) relaxation, and network constraints of DHS are considered. An iterative semi-definite programming relaxation method is utilized to ensure the solution accuracy. The effectiveness of the proposed model on improving the utilization rate of DGs and the economy is verified based on case studies.

The coordinated operation of regional integrated electricity and district heating systems (RIEDHS) can significantly improve energy efficiency. This paper considers the dynamic characteristics of the pipelines in the district heating systems (DHS) and the thermal inertia of buildings with different heating modes, and establishes a distributed optimal scheduling model of RIEDHS. In addition, due to the computational complexity of large-scale integrated energy systems, heavy communication burdens, and the privacy-preserving difficulties of multiple operators, a centralized solution is actually not feasible for RIEDHS. Based on the Alternating Direction Method of Multipliers (ADMM) algorithm, a distributed method for solving the RIEDHS problem is proposed. In order to enhance the convergence of the algorithm, the ADMM with variable penalty parameters is adopted. The effectiveness and superiority of the method are verified by case studies. The results show that the optimal scheduling model can make full use of the thermal inertia of buildings, reduce system operating costs, and promote photovoltaic consumption.

As an emerging energy storage technology for renewable power utilization, power-to-gas (P2G) requires efficient bidding strategies to increase profit. This paper proposes a bi-level strategic bidding model for P2G with the consideration of a carbon emission trading scheme (CETS), an embedded locational marginal price (LMP) and wind power uncertainty, where the upper level model aims to maximize the profit of P2G facilities and the lower level model carries out the market clearing process. Under a CETS, a P2G facility purchases electricity power according to the LMP and is assumed to sell synthetic natural gas (SNG) and carbon emission permits for revenue. The electricity market clearing process is represented by a stochastic low carbon economic dispatch (ED) model considering the CETS, P2G and wind power uncertainty. On this basis, the formulation of a CETS embedded LMP (CETS-LMP) is presented and analyzed here. The bi-level model is reformulated and converted into mixed-integer linear programming (MILP) using strong duality theory. Case studies performed on a modified PJM 5-bus system and an IEEE 118-bus system verify the effectiveness of the proposed model and demonstrate that considering the CETS can increase the profit of P2G facilities and reduce carbon emission.

The drying of energetic materials is an essential and hazardous process involving complicated heat and mass transfer. In this paper, the drying properties of Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) are investigated using a novel developed continuous dryer which can be applied for the drying of both hazardous powders and ordinary powders. The results reveal that a distinct falling rate period occurs following a short increasing rate period with no any constant rate drying period, greatly shortening the drying time. Ten different thin-drying mathematical models are tested by the drying curve fitting and Midilli-Kucuk model with the higher R, EF and the lower MBE, RMSE and χ 2 is selected as the most adequate model for describing the thin layer drying of RDX. Effective moisture diffusivities are in the range of 3.16 × 10−8–3.46 × 10−8 m2/s. It is confirmed that drying monitoring values are well inside the security perimeter with lower temperature (less than 90 °C), electrostatic charge (at a desired level) and dust (reduced by 85.6%). Compared to the original static tray drying, the continuous drying of RDX in the novel dryer exhibits a great superiority for energy saving and cost saving with the falls of 59.7% and 13.7% respectively.

An optimal scheduling model for a hybrid energy microgrid considering the building based virtual energy storage system (VSS) is developed in this paper. The VSS model is developed by utilizing the building thermal equilibrium equation taking the heat storage characteristics of building into consideration. Firstly, mathematical models of various energy systems and VSS in the hybrid energy microgrid are developed. Then, an optimal scheduling model is developed to minimize the operation costs of the microgrid. Numerical studies demonstrate that the proposed optimal scheduling model can provide the microgrid with an effective and economical scheduling scheme and reduce the operation costs.

This paper proposes a security-constrained economic dispatch (ED) model for integrated natural gas and electricity systems. Natural gas system is modeled and its security constraints are integrated into the ED model. The Gas Shift Factor (GSF gas ) matrix is defined to reflect the impact of gas supply and load of each node on pipeline flow. The objective function for ED model is to minimize power system operating costs considering coal-fired generating units operating costs and natural gas-fired generating units (NGFGU) operating costs respectively. Numerical case studies with the IEEE 30-bus system integrated with a seven-node gas system demonstrate the effectiveness of the proposed ED model.

An economic dispatch (ED) model is proposed in this study for accommodating high penetrations of wind power with the integration of battery energy storage (BES) in power systems. In the proposed ED model, a wind-storage combined system (WSCS) model is studied to collectively mitigate the output fluctuations and improve the wind power utilisation. In addition, the proposed concept of composite operating costs include the unit operation cost, environmental cost, reserve cost, compensation cost for wind power curtailment, and energy loss cost of ES. With the minimisation of the composite operating costs as the objective function of the proposed ED model with WSCS, a modified bacterial foraging algorithm (MBFA) is proposed to solve the optimisation problem based on a combination of the bacterial foraging algorithm (BFA) and the Particle Swarm Optimisation (PSO). Furthermore, the case study on the IEEE 30-bus system has been used to demonstrate the feasibility and effectiveness of the proposed ED model and the performance improvement of the MBFA algorithm over BFA or PSO alone. The comparison between different scenarios shows that the integration of ES in the proposed model effectively improves the wind power utilisation and the system efficiency.

The interconnection and coupling of integrated energy systems (IES) including electricity system, natural gas system and district heating system become increasingly tight. It brings opportunities for improving energy consumption efficiency as well as challenges on security interactions. Thus, the concepts of the IES security region (SR), which considers all the essential operational constraints of IES (including voltage magnitude constraint, thermal constraint, gas pressure constraint, gas transmission constraint, and temperature constraint), are discussed in this paper. These concepts stem from the ideas of power system security regions and are expanded to the multi-dimensional IES involving the limits and parameters of electricity, natural gas, and district heating networks. A set of optimization models are developed to explore the SR boundaries (SRBs) in the IES. Also, a piecewise approximation is proposed to represent the SRB of IES in a multi-dimensional space with a minimum set of hyperplanes. After that, the potential implementations of SR for security assessment and optimal control in the IES are presented and analyzed. Numerical results from the IES 30-20-2 × 6 system demonstrates the feasibility and effectiveness of the proposed method and verifies that it can significantly enhance the situation awareness ability of the IES.

With the integration of large-scale renewable energy including wind power into the power grid, efficient flexible resources are required for wind power accommodation. The combined-cycle gas turbines (CCGTs) are widely used in the power system with flexible operation characteristics, which leads to enhanced interdependencies between the power grid and the natural gas network. In this paper, a unit commitment (UC) model with natural gas transmission constraints and reserve scheduling considering the flexibility of CCGT with quick startup-shutdown and flexible operation characteristics is proposed, to reduce wind power curtailment and high startup and shutdown cost of conventional generation units in the power system. Considering the complex operating conditions of CCGT and the non-convexity of Weymouth equation in natural gas network, this paper adopts an improved mode-based model (MBM) for modeling of CCGT and converts the Weymouth equation into a second-order cone relaxation (SOCR) formulation with penalty term through a concave-convex process (CCP) to make the relaxation tight, thus transforming the UC model into an iterative mixed-integer second-order cone programming (MISOCP) problem to be efficiently solved. The effectiveness of the proposed model on reducing wind power curtailment and startup-shutdown cost is demonstrated through case studies.

With the increasing interconnection of active distribution network (ADN) with the integration of distributed generations (DGs) and district heating network (DHN), local electricity-heat integrated energy market provides the opportunity of coordinated energy pricing and trading with transmission system operator (TSO). The market clearing results of distribution system operator (DSO) on local electricity and heating markets will be affected wholesale market clearing. This paper proposes a bi-level optimization model for local electricity-heat integrated energy market clearing for integrated ADN and DHN, and distribution locational marginal pricing coordinated with the wholesale market clearing of transmission system. In the proposed model, the upper-level model indicates the local electricity-heat integrated market clearing by DSO and the lower-level model indicates the wholesale electricity market clearing by TSO. The locational marginal price (LMP) of the substation of the PCC point will affect the market clearing of ADN and DHN, and further affect the distribution locational marginal price (DLMP). Moreover, the market clearing actions of DSO would affect the wholesale electricity market clearing and LMP. The equilibrium of DSO and TSO is found by transforming the proposed bi-level model into a mathematical program with equilibrium constraint (MPEC) model. The MPEC formulation is further converted into mixed integer second order cone programming (MISOCP) for effectively solving. The effectiveness of the proposed model on reducing the DLMP and improving economy of ADN and DHN are demonstrated through case studies.

Static voltage stability is critical for the operation of power grids. With the penetration of renewable energies, region-based method become a choice to present the stability of a power grid. Static voltage stability region is hence a useful and intuitive tool to determine the safety margin of a power grid. The formation of static voltage stability region is however time consuming. To overcome such issue, this work proposes a relaxed decoupled direct calculation (RDDC) method to find voltage collapse points (VCPs). The direct calculation of VCPs is a Newton-Raphson type method and faces the sensitivity issue of the initial values. Boundary tracking method provides a framework to utilize the information from neighbor VCPs and hence is integrated with the proposed RDDC method. The results show that the RDDC method is able to calculating VCPs as precise as continuation power flow with less computational time. When RDDC is integrated with boundary tracking, results show that the static voltage stability region boundary can be generated with high precision, and computational performance improvement can be observed for all the test systems from nine buses to ten thousand of buses. This demonstrates the applicability and scalability of the proposed method to power systems with different scales.

This paper proposes a decentralized power system economic dispatch (DPSED) model considering carbon capture power plants (CCPPs) and carbon emission trading scheme (CETS) under a low-carbon economy. First, CETS is introduced and analyzed, and a time-varying carbon emission quota allocation method is proposed. The centralized power system economic dispatch model (CPSED) under a low-carbon economy is established with the objective function to minimize the total operation cost in which CCPPs are introduced that can capture CO2 to reduce carbon emission trading cost. Then, the centralized economic dispatch problem is reformulated into a decentralized model. It is composed of a set of sub-problems in individual areas that can preserve privacy and be solved independently. The alternating direction method of multipliers (ADMM) algorithm is employed to solve the decentralized optimization model, reducing the amount of exchanged information and preserving the privacy of information shared among participating areas. Moreover, the over-relaxed ADMM algorithm is further adopted to accelerate the convergence. Finally, case studies are performed on a 6-bus system and an IEEE 118-bus system to validate the effectiveness of the proposed model and algorithm. Numerical results demonstrate that CCPPs could reduce carbon emissions, help wind power utilization, and achieve economic operation.

This paper proposes a novel transient stability assessment (TSA) method for hybrid AC/DC systems with VSC-HVDC via port energy. The port energy of VSC-HVDC is described as the function of port state variables and control variables. The representation of port energy greatly enhances the accuracy of the transient stability study and can be extended to arbitrarily power electronics devices. At the same time, this approach can avoid formulating energy function with the complex internal controls of VSC-HVDC. Furthermore, iterative potential energy boundary surface (IPEBS) is employed to TSA. The maximum potential energy is searched on the instability of rotor angle’s swing curves as the critical energy through the dot product criterion. Then, the influence of the VSC-HVDC for transient stability is studied, including the VSC-HVDC transmission capacity of active and reactive power. The accuracy of the proposed energy function calculation is discussed via the different fault location. The feasibility and validity of the proposed method are demonstrated via the modified IEEE 39-bus hybrid AC/DC system.

This paper proposes a multi-objective approach for low-carbon economic dispatch with carbon capture power plants (CCPPs) and demand response (DR). First, brief dispatching model of CCPPs is presented. The operation mechanism of CCPPs and DR for different objectives is analyzed. Then, the multi-objective low-carbon economic dispatch model with CCPPs and DR is proposed. The objectives include minimizing the bidding cost of conventional power units and wind power, minimizing carbon emissions, maximizing utilized wind power and minimizing DR cost. The weighting coefficients of the objectives are calculated by analytic hierarchy process (AHP). The optimization model is recast as a mixed integer linear programming (MILP) formulation and solved by CPLEX. Finally, numerical results performed on the modified PJM 5-bus system verify the feasibility and effectiveness of the developed model by comparing to single objective cases.

Summary In wind-integrated power systems, the stochastic and random wind power brings more uncertainties to power system operation. To address multiple uncertain factors including wind power forecast error, generator failure, and load forecast error, a conditional value-at-risk (CVaR)-based optimal spinning reserve determination method for wind-integrated power system is proposed in this paper with consideration of both reliability and economy of system operation under a certain level of risk. The CVaR model of spinning reserve considering different uncertain factors is built and then incorporated into the coordinated scheduling model of power generation and spinning reserve in wind integrated power system. The optimization model is solved by mix integer linear programming, and the CVaR-based spinning reserve allocation and the power output of each unit are optimally determined at a certain confidence level. The proposed method can provide a trade-off between the economy and reliability of system operation with respect to the confidence level of risk. Simulation results on 10-unit system and 100-unit system demonstrate the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

This paper proposes a stochastic day-ahead scheduling framework for integrated energy systems (IES) considering uncertain wind power generation and multienergy loads. The structure and mode...

This paper proposes a low-carbon economic dispatch model with combined wind-storage system (CWSS) and carbon capture power plants (CCPPs). First, the battery energy storage (BES) is introduced into wind power plants to form a combined wind-storage system structure, aiming at improving the operation flexibility of wind power. Then, the dispatching model of CCPPs is presented. The operating characteristics of CCPPs make it possible to control the net power outputs and carbon emissions separately and flexibly. The integration of BES and carbon capture and storage (CCS) improves the operation flexibility of wind power plants and conventional power plants, respectively. The low-carbon economic dispatch model considering CWSS and CCPPs is formulated as a mixed integer linear programming (MILP) problem. Numerical results performed on the PJM 5-bus system demonstrate that the proposed model is effective on reduction of carbon emissions and wind power curtailment.

A fast distinguish method to discriminate the critical commutation failure region of multi-infeed HVDC system is proposed in this paper. Based on the nodal impedance matrix and nodal voltage interaction factor (VIF), the extinction angles of convert stations under the single-phase or three-phase to ground fault happening at any bus in AC system is first calculated. If the calculated extinction angle with regard to a certain bus is greater than the critical extinction angle, the single-phase or three-phase to ground fault at that bus will cause commutation failure of the HVDC system. The critical failure impedance boundaries on topology diagram can be therefore delimited by examining every bus in the AC system. The simulation results of a two-infeed HVDC test system verifies the effectiveness and accuracy of the proposed method which detects the commutation failure of multi-infeed HVDC system based on critical failure impedance boundaries.

In the traditional scheduling of integrated electricity and district heating systems (IEDHS) with high penetrations of wind power, large amounts of wind power curtailments can still occur. Exploring the underlying flexibilities within the existing infrastructure can be beneficial to the further integration of wind power. The thermal inertia of district heating network and aggregated buildings can provide such flexibility. This paper proposes a collaborative scheduling model of integrated electricity and district heating systems considering the thermal inertia of district heating network and aggregated buildings and a flexibility assessment method of the integrated electricity and district heating systems. With the detailed thermal model of the aggregated buildings and the transmission time delay characteristics of heating network pipelines, a thermal inertia model of the district heating systems is proposed. Then, the scheduling model considering thermal inertia of district heating network and the aggregated buildings is formulated as a quadratic programming problem, the objective function of which is to minimize the operating cost of integrated electricity and district heating systems. Four scheduling cases based on whether to consider the transmission time delay characteristics of heating network pipelines or the adjustable indoor temperature of aggregated buildings are established, and a flexibility assessment method of different cases for the electricity system and coupling component is proposed. The validity of the model is verified by cases studies. Numerical results show that the proposed optimization scheduling model can make use of the heat storage capacity of district heating network and aggregated buildings, reduce operating costs, increase the flexibility of electricity system and effectively promote wind power integration.

In this paper, a two-stage mixed integer linear programming (MILP) formulation for source-load coordinated dispatch with wind power considering peak-valley regulation and ramping requirements is proposed. First, the peak-load regulation and ramping requirements of the net load curve are analyzed. Second, the first stage optimization model considering incentive-based demand response (IDR) is formulated to optimize the net load curve, in which the objective function is to minimize the compensation costs of IDR. Several constraints are taken into account to reduce the peak-valley regulation needs and meet the ramping requirements. Then, based on the optimized load curve, the second stage day-ahead dispatch optimization model is proposed to optimize the allocation of generation sources, taking the total costs of power generation minimization as the objective function considering thermal unit reserve costs. The proposed model is applied to a 10-unit system with a wind power farm with 600MW installed capacity. Simulation results demonstrate the effectiveness and feasibility of the proposed model.

In modern power systems, the integration of variable and random renewable energy brings more challenges to real-time voltage stability monitoring and control. Loading margin sensitivity (LMS) is an index that is widely-used to evaluate voltage stability. The accuracy of LMS relies on the credibility of the data collected by local measurement units, which are used to estimate the system model parameters. Considering the noise signals and the variability of renewable generation, this paper proposes a probabilistic LMS calculation method based on bootstrap technique. Through the bootstrap, the proposed method provides a confidence interval estimate for LMS, which is effective to implement in the presence of renewable generation. Finally, the effectiveness of the proposed method is demonstrated by a TSAT simulation on the 48-machine 140-bus NPCC system.

Available transfer capability (ATC) is the transfer capability remaining in the physical transmission network for further commercial activity over and above already committed uses which needs to be posted in the electricity market to facilitate competition. ATC evaluation is a complicated task including the determination of total transfer capability (TTC) and existing transfer capability (ETC). In the deregulated electricity market, ETC is decided by the independent system operator’s (ISO’s) economic dispatch (ED). TTC can then be obtained by a continuation power flow (CPF) method or by an optimal power flow (OPF) method, based on the given ED solutions as well as the ETC. In this paper, a bi-level optimization framework for the ATC evaluation is proposed in which ATC results can be obtained simultaneously with the ED and ETC results in the deregulated electricity market. In this bi-level optimization model, ATC evaluation is formulated as the upper level problem and the ISO’s ED is the lower level problem. The bi-level model is first converted to a mathematic program with equilibrium constraints (MPEC) by recasting the lower level problem as its Karush-Kuhn-Tucher (KKT) optimality condition. Then, the MPEC is transformed into a mixed-integer linear programming (MILP) problem, which can be solved with the help of available optimization software. In addition, case studies on PJM 5-bus, IEEE 30-bus, and IEEE 118-bus systems are presented to demonstrate the proposed methodology.