Two-Stage Multi-Objective Unit Commitment Optimization Under Hybrid Uncertainties

Wang, Bo; Wang, Shuming; Zhou, Xianzhong; Watada, Junzo

doi:10.1109/tpwrs.2015.2463725

Cited by 41 publications

(23 citation statements)

References 37 publications

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“…In (6), N T is the number of hours under study. N G and N L indicate numbers of units and lines in the system, respectively.…”

Section: Problem Formulation Of Coordinated Optimization a Objementioning

confidence: 99%

“…In [5], the interplay between line operating reliability and unit commitment has been discussed in a proposed resilience-constrained framework based on Monte Carlo simulation. Besides line random failures, more uncertainties including sudden unit outages and unforeseen load fluctuations have been quantified in [6] and a multi-objective unit commitment optimization model has been presented. This model involved the worst-case analysis and can produce a less-conservative solution.…”

Section: Introductionmentioning

confidence: 99%

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Risk-Based Coordination of Maintenance Scheduling and Unit Commitment in Power Systems

et al. 2020

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The line random failure caused by multiple uncertainties has become a non-negligible factor which influences the safe operation of power systems. In this paper, a risk-based coordination model of maintenance scheduling and unit commitment is established and the objective is to minimize the total operation cost consisting of the maintenance cost, generation cost and risk cost. A random generation method is proposed to obtain line failure scenarios based on arbitrary failure rate functions. In the base case, security constraints must be satisfied while the potential overflow caused by line outages is calculated in contingency scenarios. In order to handle this mixed integer quadratic programming, a solving strategy based on three-layer iterations is applied. In the exterior iterative layer, Lagrangian relaxation technology is used to coordinate the maintenance scheduling and the risk-based unit commitment until the maintenance schedule is available for the optimized unit commitment plan. In other two iterative layers, Benders decomposition method is adopted to the further problem decomposition. In the middle layer, iterations between the unit commitment sub-problem and the risk-based dispatch sub-problem are used to guarantee the optimality of risk-based unit commitment. In interior iterations, the feasibility of unit commitment plan is checked by an optimal load shedding model. Case studies are performed on a 6-bus test system and a 118-bus test system. Results show that the total operation cost decreases by coordination while the system operating risk can be effectively controlled.

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“…In (6), N T is the number of hours under study. N G and N L indicate numbers of units and lines in the system, respectively.…”

Section: Problem Formulation Of Coordinated Optimization a Objementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk-Based Coordination of Maintenance Scheduling and Unit Commitment in Power Systems

et al. 2020

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show abstract

“…Only zero-and first-order outages are considered here, while the probabilities of higher order outages can be neglected. Supposing ∆T = 1h, EENS at period t under scenario s in the interval k 1 is given based on the probabilistic weighted summation of all load forecast error intervals, just as shown in (14).…”

Section: Calculation Of the Power System Eensmentioning

confidence: 99%

“…This deterministic means has left out multiple uncertainties, such as the wind forecast error, the load forecast error, and the forced outage rate of units [12,13]. During recent years, the stochastic assessment of the spinning reserve has been applied in many articles [14][15][16]. With this approach, the spinning reserve is optimized to satisfy the expected energy not supplied (EENS).…”

mentioning

confidence: 99%

An Optimal Dispatch Model of Wind-Integrated Power System Considering Demand Response and Reliability

Ding

Zheng

2017

Sustainability

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Demand response (DR) has become an impressive option in the deregulated power system due to its features of availability, quickness and applicability. In this paper, a novel economic dispatch model integrated with wind power is proposed, where incentive-based DR and reliability measures are taken into account. Compared with the conventional models, the proposed model considers customers' power consumption response to the incentive price. The load profile is optimized with DR to depress the influence on the dispatch caused by the anti-peak-shaving and intermittence of wind generation. Furthermore, a probabilistic formulation is established to calculate the expected energy not supplied (EENS). This approach combines the probability distribution of the forecast errors of load and wind power, as well as the outage replacement rates of units into consideration. The cost of EENS is added into the objective to achieve an optimal equilibrium point between economy and reliability of power system operation. The proposed model is solved by mixed integer linear programming (MILP). The applicability and effectiveness of this model is illustrated by numerical simulations tested on the IEEE 24-bus Reliability Test System. Sustainability 2017, 9, 758 2 of 20 with a certain proportion of the forecast load or the maximum capacity of the operating units. This deterministic means has left out multiple uncertainties, such as the wind forecast error, the load forecast error, and the forced outage rate of units [12,13]. During recent years, the stochastic assessment of the spinning reserve has been applied in many articles [14][15][16]. With this approach, the spinning reserve is optimized to satisfy the expected energy not supplied (EENS). In [17], a triangular approximate distribution model is used to quantify EENS due to the stochastic feature of wind. Then, a security-constrained unit commitment algorithm is proposed to schedule conventional units and wind generation considering probabilistic forecast models of wind power. Another day-head scheduling model involving reliability criteria is described in [18]. EENS is calculated with the historical outage replacement rates of generators or lines. MILP is utilized to deal with the proposed model. In [19], the forecast errors of both wind power and load are supposed to follow normal distributions. These errors are discretized into several intervals for a new formulation of reliability measures. In [20,21], a two-stage stochastic problem is formulated to address various uncertainties in the system, such as wind power, solar generation, loads and even electric vehicles. The sample average approximation, which is a Monte Carlo simulation technique, is utilized to deal with uncertainties for scenario generation purposes. These works have integrated the system with distribution-free uncertainties, but given a rise to the computational burden.As an effective way for peak load shedding and shaving, DR has been widely investigated in the electricity market. Consuming wind power with DR will re...

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“…Based on historical data, power load forecasting is to explore the developing law of electricity, establish models between power demand and features, then make a valid prediction of future load [1]. A lot of operations in power systems sharply depend on the future information provided by predictions, for example making a satisfying unit commitment (UC) decision [2], saving energy and reducing the cost of power generation [3]. Therefore, accurate load forecasting has far-reaching significance for this industry.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting Using Optimized LSTM Networks Based on EMD

Wang

Zhou

et al. 2018

2018 10th International Conference on Communications, Circuits and Systems (ICCCAS)

Self Cite

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Short-term load forecasting is one of the crucial sections in smart grid. Precise forecasting enables system operators to make reliable unit commitment and power dispatching decisions. With the advent of big data, a number of artificial intelligence techniques such as back propagation, support vector machine have been used to predict the load of the next day. Nevertheless, due to the noise of raw data and the randomness of power load, forecasting errors of existing approaches are relatively large. In this study, a short-term load forecasting method is proposed on the basis of empirical mode decomposition and long short-term memory networks, the parameters of which are optimized by a particle swarm optimization algorithm. Essentially, empirical mode decomposition can decompose the original time series of historical data into relatively stationary components and long short-term memory network is able to emphasize as well as model the timing of data, the joint use of which is expected to effectively apply the characteristics of data itself, so as to improve the predictive accuracy. The effectiveness of this research is exemplified on a realistic data set, the experimental results of which show that the proposed method has higher forecasting accuracy and applicability, as compared with existing methods.

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Two-Stage Multi-Objective Unit Commitment Optimization Under Hybrid Uncertainties

Cited by 41 publications

References 37 publications

Risk-Based Coordination of Maintenance Scheduling and Unit Commitment in Power Systems

Risk-Based Coordination of Maintenance Scheduling and Unit Commitment in Power Systems

An Optimal Dispatch Model of Wind-Integrated Power System Considering Demand Response and Reliability

Short-Term Load Forecasting Using Optimized LSTM Networks Based on EMD

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