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Extreme events can cause severe power system damage. Resilience-driven operation of networked microgrids (MGs) has been heavily studied in literature. There is, though, little research considering the influence of resilience on decision making for planning. In this paper, a three-level model is suggested to solve the optimal sizing problem of networked MGs considering both resilience and cost. In the first level, a meta-heuristic technique based on an adaptive genetic algorithm (AGA) is utilized to tackle the normal sizing problem, while a time-coupled AC OPF is utilized to capture stability properties for accurate decision-making. The second and third levels are combined as a defender-attacker-defender model. In the former, the suggested AGA is utilized to generate attacking plans capturing load profile uncertainty and contingencies for load shedding maximization, while a multi-objective optimization problem is suggested for the latter to obtain a trade-off between cost and resilience. Simulations considering meshed networks and load distinction into critical and non-critical are developed to demonstrate algorithm effectiveness on capturing resilience at the planning stage and optimally sizing multiple parameters. The results indicate that higher resilience levels lead to higher investment cost, while sizing networked MGs leads to decreased investment in comparison with standalone MGs sizing. [ABSTRACT FROM AUTHOR]

A good quality stability model is a key factor for accurate power system operations.Inaccurate parameters of the stability models affect the decision making which paves the way for serious consequences. Thus, it is necessary to calibrate the stability model parameters in a regular manner. There are several calibration methods in the literature which are based on simultaneous estimation of the parameters and states. However, not all of the model parameters are well estimable simultaneously. Simultaneous estimation of parameters with high collinearity may result in biased calibration results. In this paper, the trajectory sensitivity method is used to detect the sensitive parameters and construct the sensitivity matrix. Then, parameters with high linear dependency are identified using the sensitivity matrix. It is shown that, despite the high sensitivity of a parameter, its estimability degrades as the collinearity with other parameters increase. In this paper an identifiability analysis that detects the collinearity among the sensitive parameters is proposed. The proposed method is validated using WSCC 9-Bus System. [ABSTRACT FROM AUTHOR]

This paper presents a decentralized solution algorithm for network-constrained unit commitment (NCUC) in multiregional power systems. The proposed algorithm is based on our previous work in which a local NCUC was formulated for each control entity (i.e., region) and an analytical target cascading (ATC) based distributed but partially parallelized algorithm requiring a central coordinator was presented. The primary objective of this paper is to present a decentralized approach that relaxes the need for any form of central coordinator in ATC and allows fully parallelized solutions of the local NCUCs. To achieve this objective, we formulate a bilevel optimization problem for each control entity. While the upper level solves the NCUC problem of the control entity, the lower level seeks to coordinate the control entity with its neighboring regions. The lower level is a convex optimization, which can be further replaced in the upper level problem by the Karush–Kuhn–Tucker conditions. The control entities communicate directly with each other and synchronously solve their local NCUCs. Having no need for any form of central coordinator, the proposed algorithm is potentially less vulnerable to cyber-attacks and communication failures than the distributed methods utilizing a coordinator. [ABSTRACT FROM AUTHOR]

Due to the deregulation of power systems worldwide, bidding behavior simulation research has gained prominence. One crucial element in these studies is accurately defining and modelling the individual reward function (or objective function). Considering the ubiquitous information barriers between market participants and researchers, the common way is to develop reward functions based on theoretical assumptions, which will inevitably cause deviations from the real world. However, since market data have gradually become transparent in recent years, especially data regarding historical bidding behaviors, it is feasible to introduce data-driven methods to identify the individual reward functions that are hidden in raw bidding data. Thus, this paper proposes a data-driven bidding objective function identification framework with three procedures. First, the bidding decision processes of participants are formulated as a standard Markov decision process. Second, a deep inverse reinforcement learning method that is based on maximum entropy is introduced to identify individual reward functions, whose high-dimensional nonlinearity could be saved in multilayer perceptions (MLPs). Third, a deep Q-network method is customized to simulate the individual bidding behaviors based on the obtained MLP-based objective functions. The effectiveness and feasibility of the proposed framework and methods are tested based on real market data from the Australian electricity market. [ABSTRACT FROM AUTHOR]

Due to uncertainties in generation and load, optimal decision making in electrical energy markets is a complicated and challenging task. Participating agents in the market have to estimate optimal bidding strategies based on incomplete public information and private assessment of the future state of the market, to maximize their expected profit at different time scales. In this paper, we present an agent-based model to address the problem of short-term strategic bidding of conventional generation companies (GenCos) in a power pool. Based on the proposed model, each GenCo agent develops a private probabilistic model of the market (using dynamic Bayesian networks), employs an online learning algorithm to train the model (sparse Bayesian learning), and infers the future state of the market to estimate the optimal bidding function. We show that by using this multiagent framework, the agents will be able to predict and adapt to approximate Nash equilibrium of the market through time using local reasoning and incomplete publicly available data. The model is implemented in MATLAB and is tested on four test case systems: two generic systems with 5 and 15 GenCo agents, and two IEEE benchmarks (9-bus and 30-bus systems). Both the day-ahead (DA) and hour-ahead (HA) bidding schemes are implemented. The results show a drop in market power in the 15-agent system compared to 5-agent system, along with a Pareto superior equilibrium in the HA scheme compared to the DA scheme, which corroborates the validity of the proposed decision making model. Also, the simulations show that introduction of an HA decision making stage as an uncertainty containment tool, leads to a more stable and less volatile price signal in the market, which consequently results in flatter and improved profit curves for the GenCos. [ABSTRACT FROM PUBLISHER]

Traditional system investment decision is costly and hard to reverse. This is aggravated by uncertainties from flexible load and renewables (FLR), which impact the accuracy of network investment decisions and could trigger a high asset risk. Thus, system operators have the incentive to postpone network reinforcement, and ‘wait and see’ whether the request of investment can be reduced or delayed with new information. This paper proposes a novel method to evaluate network investment horizon deferral based on the trade-off between waiting profit and waiting cost under FLR uncertainties. Although deferring investment leads to waiting cost, it is worthy to wait if the cost is smaller than the waiting profits. To capture the impact of FLR uncertainties on system investment, nodal uncertainties are converted into branch flow uncertainties based on a combined cumulant and Gram-Charlier expansion method. The waiting cost is quantified by the options’ cost based on real options method and waiting profit is from asset present value reduction due to the deferral. Thus, by paying waiting cost, current investment cost can be reserved until uncertainties are reduced to an acceptable level. The waiting time is evaluated by Sharp ratio and expected return, determined by the waiting cost and uncertainty level. The results show that paying waiting cost is an economical way to reduce the impact of uncertainty and avoid hastily investment. [ABSTRACT FROM AUTHOR]

One of the short-coming challenges of power systems operation and planning is the difficulty to quantify the variability of power systems Kinetic Energy (KE) to unveil online additional information for the system operators’ decisions support. KE monitoring requires innovative methods to analyse the continuous fluctuations in the KE power’s systems. In this paper, we propose the use of information theory, specifically the concept of Information Length (IL), as a way to provide useful insights into the power system KE variability and to demonstrate its utility as a starting point in decision making for power systems management. The proposed IL metric is applied to monthly collected data from the Nordic Power System during three consecutive years in order to investigate the KE evolution. Our results reveal that the proposed method provides an effective description of the seasonal statistical variability enabling the identification of the particular month and day that have the least and the most KE variability. Additionally, by applying a Long Short-Term Memory (LSTM) neural network model to estimate the value of the IL on-line, we also show the possibility of using the metric as data-driven support. [ABSTRACT FROM AUTHOR]

For nearly 20 years, the Test Feeder Working Group of the Distribution System Analysis Subcommittee has been developing openly available distribution test feeders for use by researchers. The purpose of these test feeders is to provide models of distribution systems that reflect the wide diversity in design and their various analytic challenges. Because of their utility and accessibility, the test feeders have been used for a wide range of research, some of which has been outside the original scope of intended uses. This paper provides an overview of the existing distribution feeder models and clarifies the specific analytic challenges that they were originally designed to examine. Additionally, this paper will provide guidance on which feeders are best suited for various types of analysis. The purpose of this paper is to provide the original intent of the Working Group and to provide the information necessary so that researchers may make an informed decision on which of the test feeders are most appropriate for their work. [ABSTRACT FROM PUBLISHER]

This paper discusses the transmission planning problem in a transmission network with multiple transmission planners. Each transmission planner is responsible for a region of the transmission network and maximizes its own region social welfare taking into account the transmission planning decisions of other transmission planners. The mathematical formulation of non-cooperative transmission planning problem is proposed. This problem is modeled using the multiple-leaders single-follower game in applied mathematics. The solution concept of the worst-Nash equilibrium is introduced to solve the set-up game. Different mathematical techniques are employed to formulate the worst-Nash equilibrium solution as a mixed-integer linear programming problem. A discussion on two possible applications of the derived mathematical structure is provided. The computational complexity is also discussed. The Three-Region IEEE-RTS96 example system is employed and modified to suit the purpose of analysis. The cooperative solution where the transmission planners merge into one single transmission planner is assumed as the benchmark in this study. The cooperative transmission planning is formulated in Appendix A of the paper. The results demonstrate the utility of the proposed solution in multi-regional transmission planning. [ABSTRACT FROM AUTHOR]

Recent climate changes have created intense natural disasters, such as windstorms, which can cause significant damages to power grids. System resilience is defined as the ability of the system to withstand such high-impact low-probability events. This paper proposes a robust resilient operational schedule for active distribution networks against windstorms. In order to capture dynamic behaviors of these disasters, zonal disaster-specific uncertainty sets associated with the windstorm are proposed. Additionally, the unavailability uncertainties of N-K contingencies as well as the forecast uncertainties of load demand, wind power, and solar power are taken into account. Instead of committing micro-turbines and energy storage systems in the first stage (here-and-now) of the decision-making process, the proposed model considers these commitment decisions in the second stage (wait-and-see) of the decision-making process, which is more consistent with the fast response time of these units. Since the second stage of the proposed model has binary decision variables, recent KKT-based and duality-based methods are not applicable. Therefore, a new solution method based on block coordinate descent (BCD) and line search (LS) techniques is proposed to solve the bi-level problem. Eventually, IEEE 33-bus distribution test system is used to illustrate the effectiveness of the proposed model and solution method. [ABSTRACT FROM AUTHOR]

The appearance of the flexible behavior of end-users based on demand response programs makes the power distribution grids more active. Thus, electricity market participants in the bottom layer of the power system, wish to be involved in the decision-making process related to local energy management problems, increasing the efficiency of the energy trade in distribution networks. This paper proposes monopolistic and game-based approaches for the management of energy flexibility through end-users, aggregators, and the Distribution System Operator (DSO) which are defined as agents in the power distribution system. Besides, a 33-bus distribution network is considered to evaluate the performance of our proposed approaches for energy flexibility management model based on impact of flexibility behaviors of end-users and aggregators in the distribution network. According to the simulation results, it is concluded that although the monopolistic approach could be profitable for all agents in the distribution network, the game-based approach is not profitable for end-users. [ABSTRACT FROM AUTHOR]

This paper proposes optimal operation scheduling of a Microgrid (MG) and battery swapping stations (BSSs) as two independent stakeholders with inherently conflicting objectives. In this regard, a bi-level scheduling framework for optimal decision making of MG and BSSs is presented. Moreover, battery degradation cost is explicitly modeled based on the depth of discharge and the cycle life's intrinsic behavior of batteries. In order to tackle both historical data-based and human-related uncertainties under incomplete information including load demand of MG, photovoltaic generation, wholesale market prices, and swapping requests, a hybrid probabilistic-possibilistic approach considering correlation among uncertainties has been developed. To solve the proposed MG-BSS optimization problem, alternative direction method of multipliers (ADMM) with restart algorithm in a fully decentralized fashion is implemented. The effectiveness of the proposed model is demonstrated on a real-test MG system under different scenarios. Moreover, to compare the computational complexity of the proposed algorithm with the standard ADMM and investigate the scalability of the algorithm, extensive simulations are carried out on different standard test system. [ABSTRACT FROM AUTHOR]

This paper aims to advance decision making in power systems by proposing an integrated framework that combines sensor data analytics and optimization. Our modeling framework consists of two components: first, a predictive analytics methodology that uses real-time sensor data to predict future degradation and remaining lifetime of generators as a function of the loading conditions, and second, a mixed integer optimization model that transforms these predictions into cost-optimal maintenance and operational decisions. We model the key balance between meeting demand with very high confidence and at the same time prolonging the lifetime of generation assets. To do so, we encapsulate stochastic loading-dependent predictive models for asset condition within our optimization model. The methodology is validated and evaluated using IEEE 118-bus system that has been augmented using real-world sensor-based vibration signals from rotating machinery to emulate physical degradation of generators. Our experiments suggest that the proposed framework provides considerable improvements over conventional methods in terms of cost and reliability. [ABSTRACT FROM AUTHOR]

As an efficient tool for uncertainty quantification of renewable energy forecasting, prediction intervals (PIs) provide essential prognosis to power system operator. Merely improving the statistical quality of PIs with respect to calibration and sharpness cannot always contribute to the operational value for specific decision-making issue. In this paper, the cost-oriented prediction intervals are firstly proposed to achieve the joint improvement of forecasting quality and decision performance. In order to bridge the gap between forecasting and decision, a novel cost-oriented machine learning (COML) framework is established, which unifies nonparametric renewable power PI construction and decision-making. Formulated as a bilevel programming model, the COML minimizes the operational costs of decision-making process by adaptively adjusting the quantile proportion pair of PIs resulting from extreme learning machine based quantile regression. The hierarchical optimization model of the COML is equivalently simplified as a single level nonlinear programming problem. Then an enhanced branch-and-contract algorithm with innovative bounds contraction strategy is devised to efficiently capture the optimum of the single level problem with bilinear nonconvexity. Numerical experiments based on actual wind farm data simulate the online forecasting and decision process for wind power offering. Comprehensive comparisons verify the substantial superiority of the proposed COML methodology in terms of forecasting quality, operational value, as well as computational efficiency for practical application. [ABSTRACT FROM AUTHOR]

Network use-of-system (UoS) tariffs play an essential role in the deregulated power markets to recover network investment and maintenance costs from network users, and send economic signals to influence the users' behaviors in using systems. The tariffs are essential to the business operation of both network operators and users. They, however, could have great volatility due to the uncertainties from both external and internal factors. Such uncertain tariffs could bring severe adverse impacts to network users, which justifies that there is an urgent need to reduce the tariff volatility and improve its predictability. This paper for the first time investigates the variations in network tariffs that cause great risks to network users and then designs financial tools to reduce the volatility. The causing factors of the variations are categorized into global and local groups according to their different features. After introducing the process from network planning, charging to revenue reconciliation, the paper discusses the benefits of hedging for network users. In order to reduce tariff volatility, it proposes a novel risk management strategy to maintain the consistency of tariffs. It is achieved by designing long-term contracts using financial hedging. The value of hedge contracts is decided by three key factors: hedged load/generation percentage, hedged price, and risk premium. The hedged part is charged at hedged prices and the non-hedged part is charged at actual prices, on top of which customers needs to pay extra for the risk premium. The paper also designs an optimal decision-making tool to assist network users to manage long-term contracts in order to reduce total tariff costs. As demonstrated in the case study, the proposed long-term products can effectively reduce tariff volatility for network users and create a safe environment for their business operation. [ABSTRACT FROM PUBLISHER]

This paper develops a decision making framework for mid-term scheduling problems of the large industrial consumers. The proposed approach is based upon the recently introduced class of stochastic programming problems, established by the concept of second-order stochastic dominance (SSD). In this paper, it is assumed that the electricity price and the rate of availability (unavailability) of the generating unit (forced outage rate) are the sources of uncertainty in the decision-making problem. In the developed SSD-constrained stochastic programming problem, the consumer risk is managed by minimization of the pool procurement, as the objective function, and economic issues (e.g., cost minimization) are considered in the SD constraint. Furthermore, while most approaches optimize the cost subject to an assumed demand profile, our method enforces the electricity consumption to follow an optimum profile for mid-term time scheduling, i.e., three months (12 weeks), so that the total production will remain constant. Simulation results for a typical cement factory (as an industrial consumer) and comparison to a mean-risk approach with CVaR risk measure, revealed the interesting results and benefits of the proposed approach. [ABSTRACT FROM PUBLISHER]

The integration of variable renewable energy (VRE) generation, i.e., wind power and solar photovoltaic, brings significant uncertainty for the power system operation. Different with VRE techniques, concentrating solar power (CSP) is an appealing renewable generation technology due to its dispatch ability through the use of thermal energy storage and is thus expected to play a significant role in high renewable energy penetrated power systems. In this paper, we propose a look-ahead stochastic unit commitment model to operate power systems with CSP under high renewable energy penetration. It has a three-stage structure. The first stage optimizes the operational decisions in a day-ahead framework based on forecasts; the second stage minimizes the expected generation cost for possible realizations in the real time; and the third stage accounts for look-ahead operation in future operating days. This paper has a dual purpose: first, exploring how CSP plants operate in high renewable penetrated power systems; and second, analyzing the benefits of CSP in accommodating VRE generation. A case study on a modified IEEE RTS-79 system with actual solar and wind power data is provided to validate the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach incorporating scheduling decisions into a multinodal multiperiod Cournot game. Through applying the Nikaido–Isoda function, market clearing is conducted without dual values being required. Maps of Nash equilibria are obtained through a branch-and-cut algorithm, based on tailored cutting rules. A case study of inertial response requirements shows that these maps and the resulting range of potential player profits can be used to analyze the impacts of policy decisions influenced by discontinuous variables. The case study also shows the financial impact on neighboring producers to the node with applied inertial response requirements. [ABSTRACT FROM AUTHOR]

The growing concern over catastrophic weather events, mostly as a direct result of climate changes, has underscored the need for expanding traditional power system contingency analyses to handle the associated risks of extreme power outages. To enable power system operators to make timely decisions when facing extreme events, we explore in this paper the viability of a classifier which uses the machine learning approach based on the Bayes decision theory as a means of predicting power system component outages. However, owing to an excessively imbalanced and largely sparse power component outage datasets, the corresponding classifier learning is a challenging problem in the data mining community. In the proposed approach, we apply a resampling method to overcome the class imbalance problem. The proposed classifier provides an effective framework that not only minimizes outage prediction errors for power system components, but also considers the cost of each preventive action according to its implication in extreme events. The outcome of the proposed model can be used for introducing operation-oriented preventive measures that allow the rescheduling of generation resources for maximizing the power system resilience. The performance of the proposed classifier is examined through numerical simulations by utilizing the confusion matrix. [ABSTRACT FROM AUTHOR]

Based on well-known statistical laws characterizing the propagation of errors, and the guidelines provided by the related international standards, this paper first presents a theoretical analysis of the uncertainty associated with the conventional measurements used by weighted least squares state estimators. Three major sources of errors are considered, namely instrument transformers, measuring devices, and deadband errors, leading to algebraic expressions that clearly show the contribution of each major factor to the overall uncertainty. Then, the methodology is applied to the subtransmission systems of Unión Fenosa Distribución, and a comparison is performed illustrating the potential benefits arising from the use of the proposed weighting coefficients. [ABSTRACT FROM AUTHOR]

In this paper, an agent-based model is proposed to improve the electricity market efficiency by using different demand response programs (DRPs). In the proposed model, the strategic self-scheduling of each market player in the electricity market and consequent market interactions are considered by using a game theoretic model powered by a security constrained unit commitment. The tariffs of price-based DRPs and the amount of incentive in the incentive-based DRPs are optimized. Furthermore, a market power index and the operation cost are used to evaluate the market efficiency by using a multi-objective decision-making approach. The results show that different types of DRPs differently affect the oligopolistic behavior of market players, and the potential of market power in power systems can be mitigated by employing the proposed model for DRP optimization. Numerical studies reveal that applying combinational DRPs is more efficient when the regulatory body considers both economic and market power targets. [ABSTRACT FROM AUTHOR]

Due to the lack of information about the causes of outages, the estimation of the interactions between component failures that capture the general outage propagation patterns is a typical parameter estimation problem with incomplete data. In this paper, we estimate these interactions by the expectation maximization (EM) algorithm. The proposed method is validated with simulated cascading failure data from the AC OPA model on the IEEE 118-bus system. The EM algorithm can accurately estimate the interactions and identify the key links and key components using only a small number of the original cascades from a detailed cascading blackout model, which is critical for online cascading failure analysis and decision making. Compared with AC OPA simulation, the highly probabilistic interaction model simulation based on the proposed interaction estimation method can achieve a speed-up of 100.61. [ABSTRACT FROM PUBLISHER]

The coordination of various energy vectors under the concept of multi-energy system (MES) has introduced new sources of operational flexibility to system managers. In this paper, the behavior of multi-energy players (MEP) who can trade with more than one energy carrier to maximize their profits and mitigate their operational risks has been investigated. The MES is represented based on a multilayer structure, namely the energy market, MEP, the local energy system (LES), and multi-energy demand. In such environment, an MEP aggregates LES and participates in the wholesale electricity market, simultaneously to maximize its profit. The decision-making conflict of the MEP with other energy players for the aggregation of LES and participation in the electricity market is modeled based on a bilevel approach. Numerical results show the behavior of the MEP as a prosumer in the electricity market to produce smoother demand and price profiles. Results reveal a mutual effect of local and wholesale equilibrium prices by increasing the share of the MEP. [ABSTRACT FROM PUBLISHER]

The limitations of centralized optimization methods for power systems operation have led to the distributed computing paradigm, particularly in power distribution systems. The existing techniques reported in recent literature for solving distributed optimization problems are not viable for power distribution systems applications. The essential drawback remains a large number of required communication rounds, i.e., macro-iterations among the computing agents to solve one instance of the optimization problem; the typical number of macro-iterations are in the order of $10^2\sim 10^3$. In this paper, a new and scalable distributed optimization method based on Equivalent Network Approximation (ENApp) is proposed to solve optimal power flow (OPF) for a balanced radial distribution system. Specifically, the distribution system's radial topology is leveraged to reduce the decomposed systems into upstream and downstream network equivalents. The proposed innovations reduce the required number of macro-iterations/communication-rounds for convergence by order of magnitude. The approach is validated using IEEE 123-bus and IEEE 8500-node test systems and is shown to converge to the same optimal solution as obtained using an equivalent centralized OPF (C-OPF) model. [ABSTRACT FROM AUTHOR]

This paper describes an integrated operational simulation tool that combines various stochastic unit commitment and economic dispatch models together that consider stochastic loads and variable generation at multiple operational timescales. The tool includes four distinct configurable sub-models within: day-ahead security-constrained unit commitment (SCUC), real-time SCUC, real-time security-constrained economic dispatch (SCED), and automatic generation control (AGC). The unit commitment and dispatch sub-models within can be configured to meet multiple load and variable generation (VG) scenarios with configurable first stage and second-stage decisions determined where first-stage decisions are passed on and second-stage decisions are later determined by other sub-models in a continuous manner. The progressive hedging algorithm (PHA) is applied to solve the stochastic models to maintain the computational tractability of the proposed models. Comparative case studies, considering various configurations of stochastic and deterministic sub-models are conducted in low wind and high wind penetration scenarios to highlight the advantages of the stochastic programming during different decision-making processes. The effectiveness of the proposed method is evaluated with sensitivity tests using both economic and short-term reliability metrics to provide a broader view of its impact at different timescales and decision-making processes. [ABSTRACT FROM PUBLISHER]

Transmission charges aim to recover the cost of transmission network investments and provide efficient locational signals to new generators. In this paper, we investigate the effect of these charges on the development of new generation capacities in the system. Generation expansion planning is decided by strategic generation planners (SGPs) trying to maximize their profits, while transmission line investments are planned by a central planner and regulatory body aimed at minimizing the overall operation and network investment costs of the system. Regulatory transmission charges (RTCs) are calculated according to the marginal responsibility of generation investment on transmission network investment costs. An iterative algorithm is proposed to model the interaction taking place between the central planner and SGPs. The developed methodology is applied to a 2-node illustrative example and the IEEE-RTS96, and effects of RTCs on investment decisions of SGPs are analyzed. [ABSTRACT FROM AUTHOR]

Evolutions in the power system challenge the manner in which power system reliability is managed. In particular, currently used reliability criteria, typically the deterministic N-1 criterion, are increasingly inadequate. Moving to an alternative approach is difficult as quantifying benefits is hard in a multifaceted environment and system operators are reluctant to move away from the easy and transparent existing criterion. This paper presents a generic framework to evaluate and compare socio-economic and reliability performance of power system reliability criteria, focussing on the short term decision making process of transmission system operators (TSO). The framework can also be used to tune the parameters of reliability criteria. Short term operational planning and real time operation TSO decision making processes are simulated considering various reliability criteria. The framework is applied to a 5 node test system and the 24 node IEEE reliability test system, showing that the applied probabilistic reliability criterion outperforms deterministic N-0 and N-1 approaches in those systems in terms of expected reliability and socio-economic indicator values. The effect is larger in the bigger system with more operational flexibility. [ABSTRACT FROM PUBLISHER]

This paper proposes an integrated framework based on covariates, which coordinates short-term generation and transmission maintenance scheduling with midterm maintenance decisions by considering the effects of short-term security-constrained unit commitment (SCUC). A recursive sampling method is introduced in the proposed Monte Carlo-based framework for generating scenarios, in which the effects of component aging and covariates on the outage process are quantified by the proportional hazard model (PHM). For each sampled scenario, an iterative dynamic scenario updating approach is introduced to consider interactions among covariate conditions, random component outages, and maintenance outage scheduling. The co-optimization problem is decoupled into three separate optimization subproblems by Lagrangian relaxation (LR), which include generation maintenance scheduling, transmission maintenance scheduling, and short-term SCUC problems. Each scenario is dynamically updated based on the optimal maintenance outage and SCUC solutions, and maintenance and SCUC solutions are re-optimized using the updated scenario. The iterative procedure stops when neither the optimal schedule nor the dynamic scenario changes any further. The overall convergence of the proposed Monte Carlo-based framework is checked by the coefficient of variation (CV) of costs over multiple scenarios. Case studies on the 6-bus system and the IEEE 118-bus system are used to exhibit the effectiveness of proposed framework. [ABSTRACT FROM PUBLISHER]

In deregulated power systems, the distribution network operator (DNO) is not responsible for investment in distributed generation (DG) units, and they are just concerned about the best architecture ensuring a good service quality to their customers. The investment and operating decisions related to DG units are then taken by entities other than DNO which are exposed to uncertainty. The DNO should be able to evaluate the technical effects of these uncertain decisions. This paper proposes a fuzzy evaluation tool for analyzing the effect of investment and operation of DG units on active losses and the ability of distribution network in load supply at presence of uncertainties. The considered uncertainties are related to load values, installed capacity, and operating schedule of DG units. The proposed model is applied on a test system and also a real French urban network in order to demonstrate its functionality in evaluating the distribution expansion options. [ABSTRACT FROM PUBLISHER]

This paper provides a methodology to assist a strategic producer in making informed decisions on generation investment. A single target year is considered with demand variations modeled through blocks. The strategic behavior of the producer is represented through a bilevel model: the upper-level considers both investment decisions and strategic production actions and the lower-level corresponds to market clearing. Prices are obtained as dual variables of power balance equations. Rival uncertainties (on offering and investment) are characterized through scenarios. The resulting model is a large-scale mixed-integer LP problem solvable using currently available branch-and-cut techniques. Results pertaining to an illustrative example and a case study are reported and discussed. [ABSTRACT FROM PUBLISHER]

In power systems with a large integration of wind power, setting the adequate operating reserve levels is one of the main concerns of system operators (SO). The integration of large shares of wind generation in power systems led to the development of new forecasting methodologies, including probabilistic forecasting tools, but management tools able to use those forecasts to help making operational decisions are still needed. In this paper, a risk evaluation perspective is used, showing that it is possible to describe the consequences of each possible reserve level through a set of risk indices useful for decision making. The new reserve management tool (RMT) described in the paper is intended to support the SO in defining the operating reserve needs for the daily and intraday markets. Decision strategies like setting an acceptable risk level or finding a compromise between economic issues and the risk of loss of load are explored. An illustrative example based on the Portuguese power system demonstrates the usefulness and efficiency of the tool. [ABSTRACT FROM PUBLISHER]

This paper proposes a new unit commitment (UC) formulation for a power system with significant levels of wind generation. The proposed scheme departs from existing unit commitments in that it explicitly models the day-ahead predicted residual demand probability density function (PDF) including the effect of wind power curtailment. This PDF is then used to define a constraint on the probability of the residual demand exceeding the scheduled reserve, which is imposed in addition to the standard N-1 deterministic security criterion. This hybrid probabilistic/deterministic form maintains the mixed-integer linear structure that makes the proposed UC compatible with highly efficient commercially available solvers. Numerical examples illustrate the economical and technical benefits obtained by systematically including wind curtailment as decisions variables in the UC. In addition, the paper computes the hourly day-ahead UC schedule over the course of one year for a typical power system to illustrate the impact of wind power penetration on measures such as operation costs, incremental costs, emission levels, on/off unit switching operations, and reserve levels. [ABSTRACT FROM AUTHOR]

Traditional online security assessment determines whether the system is secure or not, but how secure or insecure is not explicitly indicated. This paper develops probabilistic indices, risk, to assess real-time power system security level. Risk captures not only event likelihood, but also consequence. System security level associated with low voltage and overload can be optimally controlled, using the NSGA multiobjective optimization method. A security diagram is used to visualize operating conditions in a way that enables both risk-based and traditional deterministic views. An index for cascading overloads is used to evaluate the Pareto optimal solutions. This paper shows that the multiobjective approach results in less risky and less costly operating conditions, and it provides a practical algorithm for implementation. The IEEE 24-bus RTS-1996 system is analyzed to show that risk-based system security control results in lower risk, lower cost, and less exposure to cascading outages. [ABSTRACT FROM AUTHOR]

This paper presents a bilevel model for describing the interdependence of decisions between a retailer/aggregator and the distributed resources owners that the former serves and represents. Decision making is performed in a hierarchical fashion. The retailer performs the energy management of various resources indirectly; price signals are used as incentives for inducing the entities owning the distributed resources in a behavior that optimizes the operation and economic profit of the microgrid that they compose. The two-level interaction is formulated mathematically as a mathematical programming problem with complementarity constraints, while nonlinearities are replaced by equivalent linear expressions. The results of a characteristic case study indicate the efficiency and scalability of the proposed model. [ABSTRACT FROM PUBLISHER]

This paper proposes a new decentralized restoration scheme in order to realize efficient coordinated restoration of the coupled transmission and distribution (TS-DS) system. First, considering the separated operation of the TS operator (TSO) and DS operators (DSOs) and to avoid the TSO data processing burden, the TS-DS system is divided into several subsystems according to their physical connection. Then, in order to implement the independent but coordinated decision-making of the subsystems, a decentralized decision-making framework is constructed by model decoupling and iterative interaction between TS and DS. Furthermore, for the sake of the convergence of the iterative process with non-convex models, a new decentralized optimization method is developed to obtain the optimal coordinated restoration strategy of the TS-DS system. The proposed decentralized restoration scheme achieves independent decision-making of the TSO and DSOs in the coupled TS-DS system, and solves the non-convergence problem of the decentralized optimization method. The effectiveness of the proposed method is validated using two IEEE standard test systems and a real-world power system. [ABSTRACT FROM AUTHOR]

Despite substantial improvement in the development of forecasting approaches, conditional and dynamic uncertainty estimates ought to be accommodated in decision-making in power system operation and market, in order to yield either cost-optimal decisions in expectation, or decision with probabilistic guarantees. The representation of uncertainty serves as an interface between forecasting and decision-making problems, with different approaches handling various objects and their parameterization as input. Following substantial developments based on scenario-based stochastic methods, robust and chance-constrained optimization approaches have gained increasing attention. These often rely on polyhedra as a representation of the convex envelope of uncertainty. In this paper, we aim to bridge the gap between the probabilistic forecasting literature and such optimization approaches by generating forecasts in the form of polyhedra with probabilistic guarantees. For that, we see polyhedra as parameterized objects under alternative definitions (under $L_1$ and $L_\infty$ norms), the parameters of which may be modeled and predicted. We additionally discuss assessing the predictive skill of such multivariate probabilistic forecasts. An application and related empirical investigation results allow us to verify probabilistic calibration and predictive skills of our polyhedra. [ABSTRACT FROM AUTHOR]

Fossil fuel generation companies (GenCos) trade in multiple uncertain energy markets: fuel and carbon markets on upstream side while electricity market on downstream side. Global economic and environmental benefits lead these markets to pursue overlapping goals, making them highly interactive. GenCos may identify optimal trading strategies for upstream and downstream trading in an integrated framework, to manage an overall secure and profitable position. Further, severe unpredictability of energy market prices may necessitate a GenCo to make trading plans which perform better meeting its goals. Under severe uncertainty of involved markets, this paper proposes Information Gap Decision Theory (IGDT) based approach to select three interrelated trading portfolios, in an integrated framework. Results from a realistic case study provide a comprehensive decision insight to address risk-averse and risk-seeking behavior of GenCo, explicitly highlighting importance of co-variation in prices of interactive markets. [ABSTRACT FROM PUBLISHER]

The overall performance of emission trading (ET), a market-based emission regulation tool, strongly relies on participants' participation and responses. In order to improve market design, it is important for policy makers to understand the participants' trading behaviors in different market environments. However, human behaviors cannot be easily modeled with conventional analytical methods due to its “bounded rationality” characteristics. In this paper, based on the complementary features between experimental and agent-based computational methods, a hybrid interactive simulation methodology is proposed to solve human behaviors related problems. Human-subjected experiment based on European Union Emissions Trading System price data in 2006 is conducted, the results show that there is no fixed emission trading interval for generation companies, and the strategic behaviors of market participants are observed. Major driving factors of emission trading are categorized into emission price, emission quantity and time related factors, which are in accordance with empirical analysis results on EU ETS 2005–2006 transaction dataset. Furthermore, more human-subjected experiments are conducted under different emission price scenarios to obtain samples for quantitative analysis. Based on thousands of samples obtained, the joint influences of driving factors on emission trading behaviors are analyzed. The quantitative analysis results obtained can reflect the trading patterns of human participants, which provide basis for constructing computer agents that can act as useful substitutes for human participants. [ABSTRACT FROM PUBLISHER]

During faulty conditions, a sustained and heterogeneous stream of information, coming mainly from field signaling devices and customer calls, arrives at the Distribution Management System. This data should be promptly and systematically processed so that the faulted section can be isolated and service restored to customers as soon as possible. This paper presents a matrix-based framework allowing the status of the distribution grid and associated protection, signaling and switching devices to be compactly modeled and tracked. By means of algebraic expressions, the proposed information management system can infer the existence of a blackout (loss of load) and assist the operator in decision making during the identification and isolation of the affected areas. The proposed framework considers the potential presence of embedded distributed generation. With the help of both a tutorial system and a real-life case study, application examples are discussed to illustrate the potential of the proposed methodology. [ABSTRACT FROM AUTHOR]

In restructured power systems, the transmission and distribution grids are autonomously utilized by independent system operator (ISO) and distribution companies (DISCOs), respectively. As the operating condition of one grid influences the decisions made by operators of other grids, the ISO and DISCOs should collaborate and cooperate with each other in order to operate the entire power system in a secure and economic manner. According to the concept of system of systems (SoS) engineering, this paper presents a decentralized decision-making framework to determine a secure and economical hourly generation schedule for a transmission system encompassing numbers of active distribution grids. Taking into consideration the physical connections and shared information between ISO and DISCOs, an SoS-based SCUC framework is designed and a hierarchical optimization algorithm is presented to find optimal operating points of all independent systems in the SoS-based power system architecture. The numerical results show the effectiveness of the proposed SoS framework and solution methodology. [ABSTRACT FROM PUBLISHER]

In a multimarket environment, a GENCO produces electricity subject to a number of factors, including physical and environmental constraints, together with trading strategies in the electricity market (EM), fuel market (FM) and carbon market (CM). To assist a GENCO to maximize its profits from EM, FM and CM, this paper proposes a dynamic decision making model with two consecutive stages. Fuzzy differential evolution algorithm is used to solve this decision-making problem. Taking transactions in the three interactive markets into account, the proposed model has been tested for a GENCO consisting of seven thermal units and a wind farm. A rational tradeoff between the profit-making and emission reduction has been demonstrated by the GENCO using the proposed model, indicating a well alignment with the intended goal of the introducing emission trading scheme (ETS). [ABSTRACT FROM PUBLISHER]

The risk management and decision making for power systems have been evolving from the qualitative methods to the quantitative methods. The traditional decision-making model based on the system average risk and the average cost has commensurated events of low-probability/high-damage and events of high-probability/low-damage, which ignores the relative importance of these events as they are viewed by the decision makers. In this paper, we propose the concepts of conditional risk indices and conditional costs of power systems, and establish a novel risk decision-making model based on these indices. The conditional indices will be helpful for the risk decision makers to thoroughly tradeoff between different risk domains according to their preferences. Further, the system risk is monetarily represented as the reliability cost by the customer composite damage function, and the decision-making model is solved by sequential Monte Carlo simulation. Finally, the modified IEEE-RTS incorporating wind energy is investigated by the proposed model and method. The effects of decision preferences, the wind power capacity and cost on the system decision making are discussed. [ABSTRACT FROM PUBLISHER]

For economic reasons, expansions of transmission networks are based on a compromise between capital cost and security of power system operation. At the same time, the power system must withstand typical highly likely contingencies. Various emergency state control systems, also called special protections, prevent emergencies following less likely contingencies. This paper describes overloads emergency state control (OLEC) used for the alleviation of overload in transmission networks near a power plant. Such control uses the functions of automated power reduction of generating units and has been implemented for a large steam power plant. The OLEC presented here uses different methods of generation curtailment for steam generation units. A case study illustrates the efficiency of the proposed control method. [ABSTRACT FROM AUTHOR]

The proliferation of distributed energy resources entails efficient market mechanisms in distribution-level networks. This paper establishes a local energy market (LEM) framework, in which electricity and hydrogen are traded. Players in the LEM consist of renewable distributed generators (DGs), loads, hydrogen vehicles, and a hydrogen storage system (HSS) operated by an HSS agent. An iterative LEM clearing method is proposed based on the merit order principle. Players submit offers/bids with consideration of their own preferences and profiles according to the utility functions. The decentralized LEM clearing process not only avoids complex calculation induced by the centralized decision process, but also preserves players’ privacy. Case studies are conducted that demonstrate that the LEM promotes local integration of renewable energy, reduces peak demand, and improves players’ utilities. Sensitivity analysis is then implemented to discuss the influences on the LEM clearing results of capacities of DGs, loads, and the HSS, as well as price of hydrogen from the hydrogen station. [ABSTRACT FROM AUTHOR]

Situation awareness is a key factor in preserving power system security, as it enables effective and timely decision-making and reactions by the operators to an incident. Insufficient situation awareness results in a delayed, incorrect or deficient response, endangering power system stability. This factor was actually identified as one of the main causes of several electrical disturbances in the last decade. This paper identifies numerous factors that govern the formation of situation awareness in a power system control center. A multi-state model based on Markov modeling is proposed for assessing the impact of insufficient situation awareness on the probability of power system blackouts. The proposed model considers the level of situation awareness and the state of the information infrastructure. The workings of this model are illustrated using the IEEE 24-bus Reliability Test System. [ABSTRACT FROM PUBLISHER]

This paper presents a hybrid multiagent framework with a Q-learning algorithm to support rapid restoration of power grid systems following catastrophic disturbances involving loss of generators. This framework integrates the advantages of both centralized and decentralized architectures to achieve accurate decision making and quick responses when potential cascading failures are detected in power systems. By using this hybrid framework, which does not rely on a centralized controller, the single point of failure in power grid systems can be avoided. Further, the use of the Q-learning algorithm developed in conjunction with the restorative framework can help the agents to make accurate decisions to protect against cascading failures in a timely manner without requiring a global reward signal. Simulation results demonstrate the effectiveness of the proposed approach in comparison with the typical centralized and decentralized approaches based on several evaluation attributes. [ABSTRACT FROM PUBLISHER]

The paper presents well-being analysis of wind integrated electric power generating systems. An approach is presented for calculating well-being indices which gives much faster results than previously published methods. The link between the unit commitment risk criterion (UCRC) and system healthy state probability (HSPC) is illustrated using two different test systems. The method should prove useful in the decision making process of choosing an appropriate UCRC. The proposed approach extends the application of probabilistic techniques in operating reserve evaluation and determination. [ABSTRACT FROM PUBLISHER]

This paper proposes a bilevel model to assist a generation company in making its long-term generation capacity investment decisions considering uncertainty regarding the investments of the other generation companies. The bilevel formulation allows for the uncoupling of investment and generation decisions, as investment decisions of the single investing generation company are taken in the upper level with the objective to maximize expected profits and generation decisions by all companies are considered in the lower level. The lower level represents the oligopolistic market equilibrium via a conjectured-price response formulation, which can capture various degrees of strategic market behavior like perfect competition, the Cournot oligopoly, and intermediate cases. [ABSTRACT FROM PUBLISHER]

In Part I of this two-part paper, we introduce several possible methods for integrating wind power, price-responsive demand and other distributed energy resources (DERs). These methods differ with respect to information exchange requirements, computational complexity, and physical implementability. A novel look-ahead interactive dispatch that internalizes inter-temporal constraints at the DERs level, and dispatches the results of distributed decisions subject to spatial security constraints, is proposed as a possible effective algorithm. This method requires only the use of today's static security-constrained economic dispatch (SCED) by the system operators. The optimization accounting for inter-temporal constraints, and ramping rates in particular, is done by the DERs while they create their own supply and demand functions. To implement this method, today's supervisory control and data acquisition (SCADA) needs to be transformed into a multi-directional, multi-layered information exchange system. [ABSTRACT FROM PUBLISHER]

If generation in a power system is insufficient to power all loads, efficient load shedding operations may need to be deployed to maintain the supply-demand balance. This paper proposes a distributed multi-agent-based load shedding algorithm, which can make efficient load shedding decision based on discovered global information. During the information discovery process, only communications between immediate neighboring agents are used. The information discovery algorithm is represented as a discrete time linear system and the stability of which is analyzed according to average-consensus theorem. According to rigorous stability analysis, convergence of the designed algorithm can be guaranteed. To improve the speed of the algorithm, particle swarm optimization (PSO) is used to optimize the coefficients for information exchange so that the second largest eigenvalue of the iteration matrix is minimized. According to the designed algorithm, total net active power and operating status of loads can be discovered accurately even with faults. Based on the discovered information, coordinated load shedding decision can be made. [ABSTRACT FROM PUBLISHER]