In this paper, a new pricing method derived from the heat-and-electricity-integrated market clearing problem, referred to as generalized locational marginal pricing (GLMP), is presented. The market clearing problem is formulated by the independent system operator (ISO) to coordinate the electric power system with the district heating system by considering time-delay effects in the heating transfer process. GLMP is explained as the shadow price related to the nodal electricity balance and nodal heat balance at the optimal solution. Without considering network constraints, a simplified market clearing problem is proposed to illustrate the price linkage between heating and electricity markets through combined heat and power joint costs and feasible regions. Rational generation units will behave exactly as the ISO predicts by maximizing their individual producer surplus. Then, a compact form of the complete market clearing problem is employed to derive the detailed components of GLMP, namely, the extended marginal generation component, marginal loss component, and marginal congestion component. Furthermore, time-delay effects are reflected in the pricing. Numerical results verify the validity of component classification in GLMP and demonstrate that the proposed method can promote efficiency improvements and reduce cross-subsidies.

Natural gas and electricity systems are becoming increasingly strongly coupled. Gas-fired units (GFUs) are replacing retired coal plants, and the power systems are more dependent on the flexibilities provided by GFUs. The GFUs' power generation capability relies on the availability of gas resources, which is jointly determined by the capacity of gas suppliers and pipeline networks. However, the gas and electricity markets are operated separately. Consequently, the GFUs are forced to "represent" the entire power system to bid on the gas market: they must make forecasts regarding future gas consumption and bear the risk of improper contracts or being unable to meet generation schedules due to occasional insufficient gas supply. When facing larger shares of renewable energies and more frequent gas network congestions, the current market framework is particularly unreliable and inefficient, as well as economically unfriendly to the investors of the GFU assets. In this paper, we try to develop a framework that can combine the two markets. By properly pricing the scarce resources, e.g., gas transmission capacity, the joint market can help us to allocate the resources more efficiently while satisfying the demands. Moreover, a more forward-looking day-ahead market clearing framework is presented by considering the uncertainty brought by renewable energies. The formulation and algorithm of the proposed joint market model will be presented, as are some case studies.

The deepening penetration of renewables in power systems has contributed to the increasing need for generation flexibility. Specifically, for short-term operations, flexibility here indicates that sufficient ramp capacity is available to respond to the varying load and intermittent generation. To address the growing needs for ramp capacity, markets for ramp products have been launched in practice such as those in California ISO and Midcontinent ISO. Sometimes, expensive fast-start units must be committed in real time to guarantee sufficient ramp capacity. Occasionally even worse, inadequate ramp capacity from the given generation portfolio might lead to curtailment of renewable generation or load shedding. Therefore, more supplies of ramp capacity are required. In fact, wind power producers (WPPs) are physically capable of offering ramp services, which gives us a potential option. In this paper, we attempt to explore the mechanism and economic impacts of including WPPs as ramp capacity providers. To conduct the analyses, a two-stage stochastic real-time unit commitment model considering ramp capacity adequacy is formulated. Case studies indicate that both the system and the WPPs can benefit in the proposed framework.

Incorporating energy-intensive enterprises (EIEs) in power system unit commitments is beneficial for both power systems and EIEs. However, power systems and EIEs are typically operated by different entities, making it impractical to incorporate a full EIE model in the unit commitment because of the increasing model dimensions and consequent calculation efficiency and privacy issues. Therefore, a robust aggregate model (RAM) of the EIE is developed in this paper to simplify the detailed EIE model as a conventional generator; which ensures the robustness of the simplified model, i.e., all possible dispatch signals from system operators can be realized by EIEs. The flexibility of EIEs and the corresponding cost are also retained in the RAM. To obtain the technical parameters of the RAM, a two-stage robust problem is developed, and a modified column-constraint-generation solution method is proposed. In addition, the economic parameters of the RAM are obtained based on the upper convex hull of an EIE's maximum cost under given dispatch signals. With the proposed RAM, the dimensions and calculation time of the unit commitment incorporating EIEs are drastically decreased, and the EIE can reduce production costs while the system operator can reduce operational costs and increase wind integration.

The impact of wind power forecast uncertainty has been amplified by the deepening wind power penetration. To guarantee system security and reliability, sufficient dispatchable generation and transmission capacities have to be reserved. Currently, research has been carried out to improve system operational performance by optimizing schedules considering uncertainty. However, most methods are designed to cover a given risk level of uncertainty, which is determined ex ante. With the increase in wind power capacity, defining the risk level a priori without considering the unit commitment (UC) may limit the scheduling efficiency. Essentially, there is no absolute standard for acceptable risk exposure. Instead, there is a tradeoff between the risk and the cost of reserve capacity. Therefore, it is necessary to develop a tool to enable a flexible and comprehensive consideration of the risk level. In this paper, by combining chance constrained programming and goal programming, a novel model based on chance constrained goal programming is proposed to optimize the risk adjustable UC problem. To facilitate an efficient solution, the proposed model is transferred into a tractable mixed integer linear programming problem by a deterministic equivalent and piecewise linearization. Case studies are performed on an IEEE 118-bus system to illustrate the effectiveness and efficiency of the model.

含储热的光热电站具有良好的可调度性,其可调度能力与实时光照功率和储热装置内存储的能量有关.当光热电站与风电组成联合系统发电时,光热电站可以削减风电的不确定性,但由于风、光功率情况在不同调度日间具有波动性,因此联合系统需要的光热电站的可调度能力和光热电站的实际可调度能力每天均不相同.文中建立了基于场景方法的光热电站与风电联合系统的多日随机调度模型.该模型充分考虑多日风、光功率预测的不确定性,进行联合系统日前自调度.最后,通过算例分析,讨论了不同预测时间尺度、储能参数取值对自调度结果的影响.

Substantial changes in the generation portfolio take place due to the fast growth of renewable energy generation, of which the major types such as wind and solar power have significant forecast uncertainty. Reducing the impacts of uncertainty requires the cooperation of system participants, which are supported by proper market rules and incentives. In this paper, we propose a bilateral reserve market for variable generation (VG) producers and capacity resource providers. In this market, VG producers purchase bilateral reserve services (BRSs) to reduce potential imbalance penalties, and BRS providers earn profits on their available capacity for re-dispatch. We show in this paper that by introducing this product, the VG producers' overall imbalance costs are linked to both their forecast quality and the available system capacity, which follows the cost-causation principle. Case studies demonstrate how the proposed BRS mechanism works and its effectiveness.

ABSTRACT:In order to more accurately reflect the value of different energy sources in the integrated energy system, better motivate users to a reasonable energy use behavior, a new multiple energy coupling pricing mechanism—nodal energy price, was put forward. Detailed research on the nodal energy price in the combined heat and power system was conducted. Firstly, according to nodal price, the definition and classification of nodal energy price were given. The energy price could be divided into three parts: energy demand component, congestion component and multi-energy coupling component. An optimal power flow model of cogeneration system was proposed and the primal dual interior point method was used to solve this problem. The Lagrange multipliers of corresponding node power balance equations in power system and heat-supply system could be taken as nodal electricity price and nodal heat price, separately. Simulation results show that the nodal energy price well explain the energy price influencing factors in integrated energy system: the energy supply node price can be determined by the energy demand component and multi-energy coupling component, while the load node price on the basis of the energy supply node price can be superimposed congestion component.

This paper investigates the scheduling horizon of power system containing wind and concentrating solar power (CSP) plants with thermal energy storage (TES). CSP plant with TES can be used to reduce the uncertainty of renewable generation and provide services to power system. However, forecast error of wind/solar is strongly related to the forecast horizon. When CSP plant with TES is incorporated into power system, the scheduling horizon of TES should be carefully discussed to fully realize its value. To investigate the scheduling horizon of a power system with wind and CSP with TES, this paper proposed a scenario-based multi-day scheduling problem aiming at minimizing system's operational cost, considering the uncertainty of wind and solar input. The results shows the potential of multi-day scheduling for power system incorporating wind and CSP plants with TES. Results also show the impacts of forecast error, scheduling horizon, TES dissipation rate and TES capacity on system operation.

To mitigate the impact of wind forecasting error uncertainty, a Chance-Constrained Goal Programming (CCGP) based day-ahead scheduling model is proposed in this paper. Compared with the traditional Chance Constrained Programming (CCP) method, the CCGP based model is more flexible, which allows higher violation probability than the predefined probability in necessary situations. In this way, the day-ahead scheduling and the uncertain range covered by reserves can be both optimized. Therefore, the reliability and economy of the system with wind power uncertainty can be considered in details with more flexibilities. In addition, because slack variables in CCGP model have corresponding physical meanings, they can provide more information to system operators. Furthermore, numerical tests are performed with the IEEE 118 bus system with wind power input. Results indicate that the proposed method achieves a good balance of cost and risk. And the total operation cost, especially the unit commitment cost, is reduced by the proposed method. Comparative evaluations of the proposed CCGP method and CCP method are presented in the paper.

Power generation uncertainty is an important characteristic of variable generation (VG) platforms, such as wind and solar power, which brings additional operational costs to the power systems. To manage this uncertainty, responsibilities should be properly allocated to encourage good behaviors of system participants, especially the VG producers. Currently, the imbalance-cost-based mechanism is most commonly used for uncertainty management. Based on this method, we consider a new mechanism in this paper for capturing the uncertainty, which may achieve a better mechanism performance. The basic idea is to allow producers to purchase generation intervals (GIs) for their potential production output. The analysis presented in this paper indicates that producers can be very responsive to this mechanism. With the proper pricing policies, producers can be encouraged to provide additional information on upcoming uncertainties to the system operators. Additionally, three strategies for pricing GIs are included in this paper. Case studies are used to demonstrate the application of the mechanism as well as its effectiveness.

This paper presented a method of identifiability analysis on transient parameters of electrical power components based on profile likelihood. Identifiability is the premise of parameter identification. Identifiability analysis can help choose data for parameter identification, and find out the parameters that can be effectively identified. The identifiability of parameter is determined by the structure of the models, as well as the severity of the disturbance and the quality of the accuracy of measurements, corresponding to the theoretical and practical identifiability respectively. Considering these two aspects, this paper took advantage of the method of obtaining confidence interval by exploiting profile likelihood to analysis the identifiability of parameters. By solving a series of optimization problem, confidence intervals of each parameter of the models can be established, and the identifiability indices can be derived, based which we can judge the theoretical identifiability and tell how well a parameter can be practically identified with ease.

Energy-intensive enterprises (EIEs) are important industrial loads,which have very good adjustment capability. Integrating EIEs into power system dispatch can not only improve the economic efficiency of the entire system,but also reduce the EIEs” electricity costs under certain mechanisms.The related issues of integrating EIEs into power system dispatch are analyzed,and the models are developed to describe the EIEs” operational and behavioral characteristics.Then the models are embedded into a security-constrained unit commitment model.On this basis,the three different dispatch modes,i.e., centralized coordinative dispatch,decomposed coordinative dispatch,and single-step coordinative dispatch,are proposed. Finally,the necessity of the coordinated dispatch is demonstrated,and the different coordination modes are compared via cases, while the benefits of coordinative dispatch are especially shown in helping accommodate large-scale renewable energy generation.

The uncertainty of wind power generation brings problems in power system operation, such as requiring more reserves and possible frequency issues. In this paper, we propose an idea of combining concentrating solar power (CSP) plants with wind farms to reduce the overall uncertainty in the joint power output. Taking advantage of the dispatchability of CSP, the uncertainty of joint power generation is expected to decrease. Based on the operational model of CSP plants with thermal storage system, we search for the narrowest but robust bounds of the joint power output with a given uncertainty of the wind power output and solar power availability, and within operational constraints of CSP plants. The problem is formulated as an adaptive robust optimization (RO) problem, containing mixed-integer variables at the second stage. We introduce an algorithm that combines a nested column-and-constraint generation (C-CG) method and an outer approximation (OA) method to solve the problem. The case studies show that robust intervals for the joint power output can be obtained, and the obtained intervals can be significantly narrower than the original intervals of wind power.

Energy-intensive enterprises (EIEs) are typical kinds of industrial loads. They consume large amounts of electricity, and are very sensitive to electricity prices. Moreover, they have very good schedulability: they own various adjustable devices and dispatchable self-owned generation units, and have great flexibility in making production decisions. The characteristics of EIEs make them potentially ideal for coordinating with power systems and gaining a win-win situation, especially when the renewable energy penetration rate is high. However, problems still remain as to how to organize this coordination. In this paper, we design a decomposed coordinative scheduling (DCS) approach in which independent EIEs and the system exchange information iteratively to achieve final settlements. Based on a general modeling of EIEs, we introduce a mathematical formulation for DCS. The corresponding algorithm is also provided. We compare DCS to other scheduling approaches in case studies. It shows that DCS can significantly improve the benefits of the two sides without harming the privacy of EIEs. (C) 2015 Elsevier Ltd. All rights reserved.

Power network modeling is traditionally performed in a centralized way, where the network components and topological relationships are manually modeled at control centers. However, in large-scale power systems, this conventional modeling approach can suffer from poor efficiency and inevitable human errors, which may affect the reliability of system models. Currently, the development of distributed applications requires detailed modeling of local systems and consistency between local and global models, which is not easy with conventional modeling. This paper presents a two-level distributed modeling approach that reflects the demands of distributed applications. It describes the processes of substation modeling, model transformation, and control center modeling. Using this modeling approach, substation models can be derived in a distributive manner at substations, while system models are automatically generated at control centers. This novel modeling approach not only adapts to the future architecture of power system operation, but it also overcomes the drawbacks of conventional modeling. In addition, it has been applied to a real power system, where it demonstrated high-efficiency performance.

The reliability of power system dynamic simulation and transient stability assessment depends on the accuracy of parameters. Parameter identification is a crucial way of obtaining accurate parameters. However, the assessment of identifiability should be carried out a proiri. This paper demonstrates a new approach to evaluate the identifiability of power system transient model parameters, which considers both the model structure and the input/output data. By exploiting the profile likelihood, confidence intervals of each parameter can be established, based on which, the identifiability indices are calculated. Numerical tests are conducted accordingly to demonstrate the performance of the proposed approach.

The impact of inertia control of doubly-fed induction generator(DFIG)wind turbines on the small-signal stability of systems is studied.Firstly,the principle of DFIG wind turbines offering inertia responses is discussed,followed by the introduction of two approaches of inertia control.Then,possible influences of two control schemes on system small-signal stability are analyzed.Finally,on the basis of simulation,the modal analysis method is adopted to analyze the influence characteristics of different control strategies and various control parameters on system small-signal stability.Analysis results show that inertia control may have an appreciable impact on the system small-signal stability.Therefore,security constraints of small-signal stability should be carefully considered when designing the control strategy and parameters. This work is supported by National High Technology Research and Development Program of China (863 Program) (No.2011AA05A101),National Natural Science Foundation of China (No.51321005) and National Science Fund for Distinguished Young Scholars(No.51025725).