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With the coordination of various distributed energy resources, microgrid has the capability to participate energy and reserve market simultaneously via a microgrid aggregator (MGA). The profit-maximizing offering problem of MGA satisfying a pure strategy Nash equilibrium (NE) is modelled in the paper as a bi-level programming to analyze the impact of MGA on the market equilibrium. A linearization method for pure strategy NE conditions is proposed, and then the bi-level problem is equivalently transformed into a mixed integer linear programming (MILP) based on Karush-Kuhn-Tucher (KKT) conditions and strong duality theorem. Numerical results demonstrate the effectiveness of proposed method and present market NE prices are influenced by the scalability and location of integrated MGA.

Following the fast development of smart grid, it is essential that appropriate pricing scheme is important to encourage customers to participate in the electricity market operation. Therefore, an optimal real-time pricing model based on demand response is presented in this paper. Finally, the IEEE 24-bus system is employed to verify the proposed model.

The reliability evaluation of integrEated energy systems becomes very important, because it is the basis of planning and operation. However, different stakeholders in integrated energy systems bring difficulty to evaluation process due to data privacy protect. In this paper, a reliability evaluation approach for multi-agent integrated energy systems via fully distributed communication is presented. Firstly, a reliability evaluation model of multi-agent integrated energy systems is established, where wind turbines, plug-in electric vehicles and gas storages are considered in the structure. Moreover, a re-dispatching model with voltage and gas pressure constraints is presented to minimize operation cost, when a contingent incident happens. Through second-order cone and Big M relaxation, the re-dispatching model is reformulated to a solvable one. Furthermore, a state assessment method with fully distributed communication is proposed to reduce information exchange, which is presented based on Monte Carlo simulation and alternating direction method of multipliers with Gaussian back substitution. Reliability indices of each agent and whole system can be calculated via a little non-privacy message transferring. Finally, the proposed model and approach are tested on a modified MA-IES. The relevant failure among different sub-systems is analyzed and the effectiveness of fully distributed communication is validated.

Wind parks always produce diverse percentages of their nominal power at the same time, leading to a concern about correlation between wind speeds. The assessments of wind speed correlation have been particularly focused on probabilistic modeling of aleatory uncertainty. However, poor historical data, imprecise parameter estimation and incomplete knowledge of wind speeds lead to another type of uncertainty, possibilistic uncertainty, which requires an explicit analysis. Therefore, a fuzzy copula model is firstly proposed to express the possibilistic uncertainty of wind speed correlation. The advantage of the proposed model is that the copula parameters can be interval numbers, triangular or trapezoidal fuzzy numbers based on the wind speed data and subjective judgment of decision makers. For estimating copula parameters, a complete decision rule and interval estimation method is developed based on cumulative probability and probability distributions of correlated wind speeds. The effectiveness of the proposed model is validated by the application in wind curtailment evaluation while a method is developed to evaluate and quantify wind curtailment in a hybrid power system involving different types of generation. The results demonstrate that the proposed model and method are capable of describing the possibilistic uncertainty and evaluating its effect on wind curtailment. Compared with previous research, the proposed model develops a new universal parameter estimation method and selection rule to provide more interval results, by calculating the membership function of copula parameters and wind curtailment. System planners and operators can apply the fuzzy results to various topics like reserve capacity evaluation or real-time dispatch depending on their level of risk tolerance.

Reliability evaluation of Integrated Energy Systems (IESs) based on Smart Agent Communication (SAC) is studied in this paper. The typical structure and reliability evaluation modeling for IESs is firstly introduced. A new reliability evaluation approach is then presented, in which SAC based system reconfiguration is innovatively integrated into the reliability evaluation process. Based on this combination, state evaluation (key procedure of the reliability evaluation) along with system reconfiguration can be conducted autonomously in reliability evaluation. Algorithm and procedures of the system reconfiguration based on a decentralized agent communication algorithm ( K −1 algorithm) is described. The system reconfiguration does not depend on global information of whole system, which can effectively improve reliability evaluation and modeling efficiency. The presented models and approaches are conducted on the multi-paradigm modeling and simulation platform-AnyLogic, and validated by extensive cases studies.

For power system decision makers, they should decide a reasonable wind capacity or target penetration level considering the balance of wind curtailment, investment, system reliability and social benefit. Assessing wind curtailment under different wind capacity, considering the wind resource uncertainty, is therefore necessary for utilizing wind power effectively. In this paper, fuzzy wind capacity factor is proposed to analyze and propagate the uncertainty of wind resources based on the wind speed distribution. By investigating the maximal wind power output that can be accepted in candidate system nodes, a fuzzy linear programming problem is proposed to decide the required wind capacity considering the fuzzy wind capacity factor. Based on the cumulative distribution function of wind capacity resulting from large number of simulations, the relationship between wind capacity and wind curtailment is determined. The effectiveness of the proposed model is validated by its application in two power grids. The results demonstrate that the proposed model and method are capable of describing the possibilistic uncertainty of wind resources and assessing wind curtailment under different wind capacity. Compared with previous research, this method can aid power system planners to allocate wind capacity considering the possibilistic uncertainty of wind resources.

With the increasing demand-side participation in electricity market, as a profit-seeking market participant, load-serving entities (LSEs) have been trying to apply demand response (DR) programs to induce the demand elasticity to further their profit. However, due to the different preference of DRs, it is difficult for LSEs to generate the optimal strategic bidding strategy considering DR in the ISO/RTO’s market. Therefore, this paper proposed a bi-level optimization model with the consideration of demand response bidding to maximize the total profit of LSEs: 1) conceptually, different from previous related works, the consumers participate DR through setting their bidding prices to LSEs with respect to their own preference and LSEs should determine the optimal reward value of DR as well as the amount of demanded electricity; and 2) technically, an original method has been implemented to solve the bi-level optimization model. The closed form of shadow price function with respect to the total load demand is derived to reduce the complexity of the proposed bi-level model. Hence, the proposed model is converted to a mixed integer second order cone programming and able to achieve the global optimality. It needs to be note that the closed form of shadow price introduced in this paper can also be applied to other bi-level programming models. Moreover, case studies have been performed to demonstrate the validity of the proposed method: 1) the proposed method to obtain the closed form of real-time price is verified on a 9-bus system; 2) 118-bus test system with three demand response participants is tested to show that by the proposed method, LSE can benefit from the DRs under various circumstance.

Multi-energy complementarity is beneficial to reduce the operating cost and improve the reliability of energy systems. This paper presents an optimization framework for the day-ahead dispatch of distributed integrated energy system (DIES), to explore the interaction strategy of user side storage devices participating in the economic dispatch of DIES. Firstly, the model of DIES is established based on the concept of energy hub. Then, on this basis, the optimization model for the day-ahead dispatch of a DIES is built to achieve the lowest operation cost. Furthermore, the feasibility of the proposed method is validated by extensive cases studies. Results show that multi-energy complementary and energy storage devices can reduce the operating cost of energy systems effectively.

The load forecasting plays a more and more important role in the operation of the power system and the demand side. However, artificial intelligence techniques are complex in the short-term forecasting. For this purpose, this paper proposes the load forecasting model based on higher order partial least squares, which is much simpler than artificial intelligence techniques in complexity. Considering the nonlinear relationship between dependent variables and independent variables, an extended input tensor is employed in the load forecasting model. Finally, load data of year 2015 in the ISO New England is used to verify the rationality and feasibility of proposed method. Simulation results of four days that belong to four seasons separately have shown that the proposed model is very suitable for short-term load forecasting.

Optimally dispatching Photovoltaic (PV) inverters is an efficient way to avoid overvoltage in active distribution networks, which may occur in the case of PV generation surplus load demand. Typically, the dispatching optimization objective is to identify critical PV inverters that have the most significant impact on the network voltage level. Following, it ensures the optimal set-points of both active power and reactive power for the selected inverters, guaranteeing the entire system operating constraints (e.g., the network voltage magnitude) within reasonable ranges. However, the intermittent nature of solar PV energy may affect the selection of the critical PV inverters and also the final optimal objective value. In order to address this issue, a two-stage robust optimization model is proposed in this paper to achieve a robust optimal solution to the PV inverter dispatch, which can hedge against any possible realization within the uncertain PV outputs. In addition, the conic relaxation-based branch flow formulation and second-order cone programming based column-and-constraint generation algorithm are employed to deal with the proposed robust optimization model. Case studies on a 33-bus distribution network and comparisons with the deterministic optimization approach have demonstrated the effectiveness of the proposed method.

In this paper, a new reserve model based on risk of the power system is proposed to considering both the reliability and the economy. In the meantime, the emergency DC power control are considered in this model. In the reserve model, the operating reserve are classified into load reserve and contingency reserve. The objective is to minimize the energy and reserve cost. Apart from system wide reserve requirements in the previous optimization model, constraints include emergency DC power support and contingency reserve requirements reflecting by risk. This model taking the reliability and the economy into account is insightful to guide the future development of reserve schedule for power system.

In this paper, the concept of Energy Hub was introduced to capture the coupling between multiple energy forms such as electricity, gas, heat and cooling in an Integrated Energy Systems (IESs). The reliability model for Energy Hubs was established based on a Smart Agent Communication (SAC) algorithm. In the model, the energy conversion efficiency, failure rate and repair time for various energy supply systems were considered, and thus the effects of coupling among different energy types on the IES reliability were taken into account. According to the SAC algorithm, an Energy Hub was defined as a smart agent which can communicate with other smart agent. Combined with the Monte Carlo simulation, a reliability evaluation approach is presented based on the SAC algorithm and Energy Hub model. In the presented approach, fault localization, fault isolation, system reconfiguration and fault recovery can be implemented autonomously, which effectively improves the system status assessment efficiency during the reliability evaluation of IESs. Finally, the proposed models and approaches are applied on the multi-paradigm modeling and simulation platform-AnyLogic, and the effectiveness of the model is validated by extensive cases studies.

Whole series of fac- [Fe II (CN) x (CO) 3 I (3 − x ) ] − ( x = 1–3) generated by stepwise replacement of iodide from fac- [Fe II (CO) 3 I 3 ] − by cyanide were isolated and structurally characterized. The infrared trend of this series demonstrates the effect of the electronic properties of ligands on the coordinated CO stretching frequencies.