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Equipment maintenance decision is a very critical technology in power grid asset management. The traditional maintenance decision focuses on the analysis of the operation performance of the equipment itself, but lacks the analysis and description of the fuzziness of power equipment outage parameters. Therefore, this paper establishes the reliability assessment model based on equipment health condition monitoring, which makes the maintenance decision transition from equipment level to system level, and the fuzzy mathematics theory is introduced to establish a model describing the fuzziness of equipment failure rate parameter to make system reliability assessment and equipment maintenance decision scheme. Firstly, this paper proposes a fuzzy failure rate model of power transformer based on condition monitoring. Then the fuzzy parameters are combined with the conventional probabilistic reliability assessment method to establish the fuzzy probability hybrid reliability assessment model of the transmission system, and the fuzzy maintenance reliability benefit index is defined and deduced. Finally, a maintenance strategy of transformer based on fuzzy probability hybrid reliability assessment and fuzzy set theory is proposed, and the case study is carried out on a regional power grid in East China. The results show that the proposed model provides an improved maintenance strategy of power equipment. [ABSTRACT FROM AUTHOR]

This paper presents a maintenance strategy for overhead lines based on condition monitoring and system reliability assessment. A quantitative relationship between the condition monitoring data and failure rate of overhead lines is established. The system reliability is evaluated using monitoring data; the reliability benefit due to the maintenance of each line is quantified; and the maintenance ranking order is created using the benefit/cost ratio index. The proposed strategy can provide the largest reliability improvement at the lowest maintenance cost and within budget. Case studies were performed on a 182-bus, 5474-MW transmission system located in southwest China. [ABSTRACT FROM PUBLISHER]

Abstract: A new bidding based generating unit maintenance scheduling method under the electricity market environment is presented in this paper. The method can balance overall reliability and economy, and ensure the fairness to all generation companies (GENCOs). The unit maintenance scheduling is modeled as a triple-objective non-convex optimization problem with discrete variables and is solved using an improved version of the Non-dominated Sorting Genetic Algorithm (NSGA-II). A non-inferior solution set is directly produced from huge combinations. A few candidate solutions are selected from the non-inferior set by independent system operator (ISO) based on the overall reliability and economy, whereas the final maintenance schedule is determined by voting of GENCOs. The shares of vote are calculated from the bidding information of GENCOs and the right of each GENCO is fairly guaranteed. A case study of 50 units demonstrates the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract The DBF algorithm of sporadic task systems on multiprocessors uses the approximation of the exact demand bound function on uniprocessor as a criterion. The systems which are feasible under the partitioned paradigm are flagged as 'infeasible' sometimes. In this paper, we present a novel efficient DBF( eDBF) partitioned scheduling algorithm. A criterion which tracks the demand bound function exactly as needed is used to avoid the incorrect judgment in determining whether a processor can accommodate an additional task in the new algorithm. We give the pseudo code of the new algorithm on least-number processors and fixed-number processors respectively. Then, we prove the correctness of, and evaluated the effectiveness of this new algorithm. The experimental results demonstrate that eDBF has better performance than DBF and Density algorithms. [ABSTRACT FROM AUTHOR]

This article presents a quantified method to evaluate an overall condition index of power equipment and its probability density distribution using monitored data. A special transformation is developed to normalize the monitored data of different parameters. The overall condition index can be easily calculated using the normalized parameters. To capture the uncertainty and randomness of the monitored data, the kernel density evaluation method is used to assess the probability distribution of the condition index. With the probability density function, the probability of power equipment operating in the normal condition can be assessed. The monitored data of two breakers for five monitored parameters are used in the case study. The results indicate that the proposed method can straightforwardly identify which breaker has a worse performance and, therefore, should be considered for maintenance first. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) is an effective price-based demand response strategy. A reasonable design of TOU strategy can effectively reduce the peak-valley difference, and then produce a lot of benefits (such as delaying power grid investment, reducing interruption cost, and improving reliability). However, changing peak-valley period has a great influence on the peak-valley difference and power supply reliability of power system. Therefore, this paper aims to investigate the optimal modification of peak-valley period considering reliability loss under multiple TOU schemes. Firstly, this paper presents a clustering model and algorithm of optimal load curve based on a minimum error iteration method. Secondly, an optimal modification of peak-valley period based on a dynamic load point method is proposed, and the traditional peak-valley difference is replaced by the global peak-valley difference to calculate the objective function. Thirdly, this paper establishes a load–reliability relation fitting model based on the back propagation neural network. Finally, the effectiveness and correctness of the proposed method are investigated by the Roy Billinton test system and reliability test system. [ABSTRACT FROM AUTHOR]

• A flexibility enhancement pre-selection strategy based on network flexibility balance degree and line outage distribution factor is proposed. • The available flexibility index (AFI) is proposed to quantify the supply of system flexibility. • An optimization model of power system flexibility enhancement method based on pre-selection of structure adjustment strategy is proposed. The flexibility enhancement method of power system usually focuses on node flexible resource, without considering the transmission process. It may result in some flexible resources not being transferred, thus affecting the accuracy and effectiveness of the flexibility enhancement. In this paper, a flexibility enhancement optimization model based on pre-selection method of structure adjustment strategy is proposed, which can improve system flexibility with guaranteed availability of flexible resources. Firstly, the flexibility enhancement pre-selection method based on power system topology adjustment mathematical formulation is proposed to select the lines suitable for topology adjustment. Secondly, a flexibility enhancement optimization model considering economy and available flexibility is developed, which takes the maximum available flexibility index as the objective function and ensures the system flexible and economical by cost-floating constraint. The available flexibility deviation constraint corrects the system flexibility supply to guarantee the accuracy of available flexibility. Finally, the effectiveness of the proposed method is investigated through the case study on modified IEEE 24-bus and IEEE 118-bus system. [ABSTRACT FROM AUTHOR]

Time of use pricing strategy can not only reduce the load fluctuation but also improve the power system reliability. Generally, before carrying out a time of use pricing optimization, it needs to perform a period partition optimization for dividing peak‐valley periods. In existing researches, however, the period partition optimization and the time of use pricing optimization are implemented independently. Although the output of period partition optimization is used as the input of time of use pricing optimization, the bidirectional feedback effect between period partition optimization and time of use pricing optimization is not considered, which will lead to the existing time of use pricing optimization may not be the globally optimal solution. Therefore, this paper investigates a new optimization method for time of use pricing optimization with the consideration of bidirectional feedback effect, establishes a comprehensive customer satisfaction degree model considering the customer load proportion coefficient, and defines an elastic cofactor to describe the electricity price response difference of multiple types of customers. The Roy Billinton test system is used to verify the correctness and effectiveness of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Natural gas, as a clean fossil energy, has been widely used in power generation system by converting chemical energy into electrical energy through gas turbines. However, the natural gas pipeline network system (NGPNS) is comprised of many equipments, and the failure of any equipment will probably cause the outage of gas turbines. Therefore, this study proposes an analytical method which can incorporate the uncertainty of NGPNS into the reliability assessment of integrated energy system. Firstly, on the basis of an analytical technique, NGPNS's operating states and the corresponding state probabilities are simulated. Secondly, a state transferring and equivalence technique is proposed for transferring the unreliability of NGPNS to the involved outage of gas turbines. Thirdly, an equivalent reliability model of gas turbine is established for taking into account the uncertainty of NGPNS. Finally, the NGPNS is connected to the Roy Billinton Test System through the coupling of gas turbines between electrical and gas networks. The results of case studies verify the correctness and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• A method of period partitioning based on the silhouette coefficient is proposed. • A TOU price optimization model considering benefits and reliability is established. • A multi-objective simulated annealing particle swarm optimization algorithm is developed. • A bi-level TOU price optimization model for high reliability demand system is presented. The reliability of power system will vary with different demand response strategies, and it is significant to make a decision on demand response for satisfying differential reliability demand of power system. First, a peak-valley period partition model with an objective of maximizing silhouette coefficient was established based on the fuzzy clustering and step-wise iteration technology. Second, this paper proposed a multi-objective time of use electricity price optimization model considering the interests of both supply and demand sides, as well as reliability demand, and a multi-objective simulated annealing particle swarm optimization algorithm was developed to obtain solution. Third, the Pareto front curve of the dual-objective function was fitted by using a third-order Hermite interpolation algorithm, and the time of use prices corresponding to the reliability demand was obtained by using the back propagation neural network algorithm. Finally, the proposed model and algorithm were verified by the RBTS system, and the results indicate good rationality and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• The composite flexibility constraints are established to ensure the adequacy and availability of node flexible resources. • A two-stage robust optimization method of unit commitment with wind power generation considering composite flexibility is proposed. • The network flexibility balance degree (NFBD)& key lines constraint is developed to guarantee the system network balance level and network flexibility of key lines. The availability of power system flexible resources depends on the network flexibility, which plays a vital role in operation security of power systems with large-scale renewable energy. In this paper, a robust optimization method of unit commitment with wind power considering the composite flexibility constraints is proposed, which can ensure the availability of node flexible resources and then improve composite flexibility of power system. Firstly, the composite flexibility constraints of unit commitment problem are established. The composite flexibility reflects the mechanism of the interaction between node flexibility and network flexibility, which can consider the availability of node flexible resources and enable more accurately assessment of the system operation flexibility. Secondly, the concept of network flexibility balance degree is proposed to guarantee the overall network flexibility balance, and the network flexibility balance degree and key line constraints are developed to guarantee the system network balance level and the network flexibility of key lines, while improving the convergence speed of solving algorithm. Finally, the effectiveness of the proposed method is investigated through the case study on modified IEEE 24-bus system. [ABSTRACT FROM AUTHOR]

• An air conditioning load model suitable for the solution of the microgrid dispatching model is established. • A two-stage stochastic robust optimization model of microgrid with controllable air conditioning load is established. • The equivalent capacity of ESS of controllable air conditioning load under different dispatching temperatures is quantitatively analyzed. The wide application of wind power generation and photovoltaic (PV) power generation has greatly increased the uncertainty of power grid. Under the background of smart grid, peak shaving can be carried out with the help of users' equipment. This paper studies the cooling efficiency of air conditioning from the heat transfer process between room and outdoor, and establishes the air conditioning load control model. Based on this, a stochastic robust day-ahead dispatching model for microgrid is proposed, taking cost minimization as objective function. The day-ahead dispatching is considered as the first stage which considering the arrangement of electricity purchasing and generating of thermal power units. The real-time dispatching is regarded as the second stage. Based on the results of the first stage, real-time dispatching of the various situations of the renewable energy output can be carried out. The decision variables are air conditioning control and ESS charge and discharge arrangement. Then the results are fed back to the first stage, and the cost is minimized by iteration. In this paper, the double-layer problem in the second stage is transformed into a single-layer problem by mathematical method, and the Column-and-Constraint Generation (CCG) algorithm is used to solve the two-stage stochastic robust optimization model. Finally, based on the data of a microgrid in a central city, the effectiveness of the method is verified, and the economic superiority of the day-ahead dispatching model at stochastic and robust level is discussed. [ABSTRACT FROM AUTHOR]