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An uninterruptible power supply (UPS) can reduce power failure loss by responding to power demand during distribution network failures. For reliability-sensitive power consumers, deploying UPSs is a solution for improving power supply reliability. Nevertheless, because of UPS devices’ energy limits and potential failure probability, power consumers with UPSs cannot guarantee 100% adequacy. In future power systems with frequent reconfiguration and massive storage facilities, the power supply adequacy of a consumer with respect to the network configuration and the storage capacity will need to be frequently evaluated. Furthermore, the analytical formulation of a consumer with a UPS should be investigated since the simulation-based method might witness low efficiency and poor convergency. Therefore, this paper proposes a novel analytical availability index calculation framework for adequacy evaluation. The framework models the uncertainties of distribution networks and UPS devices as stochastic processes. Then, the availability index calculation formula is acquired based on the stochastic modeling. Compared with existing methods, this paper incorporates UPS devices’ capacity and characteristics. Moreover, the distribution network configuration and protection device placement are considered. Case studies on an illustrative distribution network, the modified RBTS system, and a real distribution system in China validate the proposed method. [ABSTRACT FROM AUTHOR]

Achieving the low-carbon transition of power systems requires the coordination of renewable energy generation and other emerging electricity technologies. Their technical indicators are changing rapidly since these technologies are in the initial stages of development. Determining the technical and policy targets for achieving the low-carbon transition is critical for regulators and power system planners. This paper defines this problem as a backcasting problem, whose concept is derived from environmental science research. We introduce it into the power system planning field and innovatively model it as a bi-level optimization problem. According to the characteristics of the backcasting problem, an improved barrier method is proposed to solve it efficiently. The effectiveness of the proposed model and algorithm are verified through case studies on a modified Garver’s 6-bus system and a realistic Northwest China power system, and they are compared with state-of-the-art solution techniques. The results of the backcasting problem provide a new perspective on the economic impacts of the low-carbon transition and are helpful in energy policy formulation for power systems. [ABSTRACT FROM AUTHOR]

Coordinated preheating can potentially enhance the flexibility of the electricity system, thereby driving significant economic savings. In this context, this paper proposes a novel game-theory based preheating coordination scheme aimed at guaranteeing the effectiveness of collective preheating performed by a large population of households. By adopting an innovative two-phase iterative algorithm, individual households autonomously schedule their heating power allocation to seek for savings in energy bills while smartly maintaining thermal comfort. Meanwhile, the total electricity generation cost decreases at each iteration and converges to an equilibrium solution asymptotically. Compared to the centralised optimization-based energy management control methods, the proposed algorithm effectively reduces the information exchange while significantly reduces computational complexity. The simulation results indicate that the proposed coordination strategy can drive 12.30% cost saving when all households participate in the preheating scheme whereas 11.35% cost increase will be incurred if coordination is absent. Overall, the proposed preheating control algorithm simultaneously benefits both individual households and the whole system by intelligently linking local behaviour with global interests. [ABSTRACT FROM AUTHOR]

In the last decade, a number of severe urban power outages have been caused by extreme natural disasters, e.g., hurricanes, snowstorms and earthquakes, which highlights the need for rethinking current planning principles of urban energy systems and expanding the classical reliability-oriented view. In addition to being reliable to low-impact and high-probability outages, power system should also have high level of resilience to withstand high-impact and low-probability (HILP) events. Compared with power system, multi-energy systems (MESs) have advantages in improving resilience through energy shifting across multiple energy sectors, a variety of generalized energy storage resources and thermal inertia of heat/cooling loads. This paper proposes a resilience-oriented planning method to determine optimal configuration of distribution level MES, e.g., urban energy supply systems, considering comprehensive impacts from supply, network and demand sides in MES. Impacts of energy shifting at supply side, pipe storage at network side and thermal inertia at demand side are described in the same linear modeling framework using energy hub (EH) model. Generalized energy storage resources including centralized and distributed energy storage devices, pipe network storage and building heat capacity are all modeled into centralized energy storage to facilitate an efficient configuration planning of MES. [ABSTRACT FROM AUTHOR]

There is an opportunity in modern power systems to explore the demand flexibility by incentivizing consumers with dynamic prices. In this paper, we quantify demand flexibility using an efficient tool called time-varying elasticity, whose value may change depending on the prices and decision dynamics. This tool is particularly useful for evaluating the demand response potential and system reliability. Recent empirical evidences have highlighted some abnormal features when studying demand flexibility, such as delayed responses and vanishing elasticities after price spikes. Existing methods fail to capture these complicated features because they heavily rely on some predefined (often over-simplified) regression expressions. Instead, this paper proposes a model-free methodology to automatically and accurately derive the optimal estimation pattern. We further develop a two-stage estimation process with Siamese long short-term memory (LSTM) networks. Here, a LSTM network encodes the price response, while the other network estimates the time-varying elasticities. In the case study, the proposed framework and models are validated to achieve higher overall estimation accuracy and better description for various abnormal features when compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Cellular Base Stations (BSs) are equipped with backup batteries. These batteries have some spare capacity over time while maintaining the power supply reliability, so they are potential flexible resources for power systems. This letter exhibits the insight to explore the BS dispatch potential towards power system frequency regulation. For each BS, the feasible dispatch boundaries of participating in frequency regulation are estimated. Then a framework is proposed to coordinate BSs to provide frequency support. By incorporating massive distributed BSs, the power system frequency performances can be improved. [ABSTRACT FROM AUTHOR]

One of the key steps for optimal bidding in power markets is to estimate the rivals’ bidding behaviors. However, for most participants, it would be difficult to directly forecast the rivals’ individual bids due to the information privacy and volatile characteristics of individual bidding behaviors. From another point of view, the aggregation of individual bids, denoted as aggregated supply curve (ASC), might be helpful to offset the uncertainties of individual bidding behaviors and can be used as reference for optimal bidding. In fact, the real ASC data contains bidding information from thousands of participants, which would be formulated with high dimensionality and unstructured formats, not applicable for general forecasting methods. Thus, a novel data-driven ASC forecasting framework based on long-short term memory (LSTM) model and corresponding data processing techniques is proposed in this paper. In detail, A paradigmatic data integration method is proposed to fix the unstructured data formats. A feature extraction method is developed to simplify the high dimensionality of ASC. Then, a LSTM model is customized to forecast ASCs. At last, real data from Midcontinent Independent System Operator market in the U.S. are utilized to demonstrate the forecasting performance of the proposed framework. [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]

Battery-based Energy Storage Transportation (BEST) is the transportation of modular battery storage systems via train cars or trucks representing an innovative solution for a) enhancing Variable Renewable Energy (VRE) utilization and load shifting, and b) providing a potential alternative for managing transmission congestions. This paper focuses on point b) and proposes a long-term transmission-planning model coordinated with both stationary and mobile storage units. The planning-problem objective function minimizes the total system cost, i.e., the sum of i) the investment cost of candidate transmission lines, stationary and mobile storage systems, and ii) the operation cost, including conventional generating units fuel consumption, load shedding penalty and BEST transportation costs. An alternative approach for BEST vehicle scheduling problem is implemented. The contribution lies in the accomplishment of the spatial-temporal scheduling of the mobile storage units by including the Number-of-nonzero mathematical function in the optimization model set of constraints instead of using additional binary variables as generally accomplished. The identification of either storage systems optimal location, or both optimal location and size of storage systems is also allowed. BEST usefulness is analyzed and discussed for a test-system emulating a reals system in China-Northwestern-grid with high VRE penetration divided in five regional areas, of which the most promising one for BEST implementation is identified. [ABSTRACT FROM AUTHOR]

Cellular base stations (BSs) are equipped with backup batteries to obtain the uninterruptible power supply (UPS) and maintain the power supply reliability. While maintaining the reliability, the backup batteries of 5G BSs have some spare capacity over time due to the traffic-sensitive characteristic of 5G BS electricity load. Therefore, the spare capacity is dispatchable and can be used as flexibility resources for power systems. This paper evaluates the dispatchable capacity of the BS backup batteries in distribution networks and illustrates how it can be utilized in power systems. The BS reliability model is first established considering potential distribution network interruptions and the effects of backup batteries. Then, the analytical formula of the BS availability index is derived with respect to batteries’ backup duration. The dispatchable capacity of BS backup batteries is evaluated in different distribution networks and with differing communication load levels. Furthermore, a potential application, daily operation optimization, is illustrated. Case studies show that the proposed methodology can effectively evaluate the dispatchable capacity and that dispatching the backup batteries can reduce 5G BS electricity bills while satisfying the reliability requirement. [ABSTRACT FROM AUTHOR]

Dynamic Security Assessment (DSA) for the future power system is expected to be increasingly complicated with the higher level penetration of renewable energy sources (RES) and the widespread deployment of power electronic devices, which drive new dynamic phenomena. As a result, the increasing complexity and the severe computational bottleneck in real-time operation encourage researchers to exploit machine learning to extract offline security rules for the online assessment. However, traditional machine learning methods lack in providing information on the confidence of their corresponding predictions. A better understanding of confidence of the prediction is of key importance for Transmission System Operators (TSOs) to use and rely on these machine learning methods. Specifically, from the perspective of topological changes, it is often unclear whether the machine learning model can still be used. Hence, being aware of the confidence of the prediction supports the transition to using machine learning in real-time operation. In this paper, we propose a novel Conditional Bayesian Deep Auto-Encoder (CBDAC) based security assessment framework to compute a confidence metric of the prediction. This informs not only the operator to judge whether the prediction can be trusted, but it also allows for judging whether the model needs updating. A case study based on IEEE 68-bus system demonstrates that CBDAC outperforms the state-of-the-art machine learning-based DSA methods and the models that need updating under different topologies can be effectively identified. Furthermore, the case study verifies that effective updating of the models is possible even with very limited data. [ABSTRACT FROM AUTHOR]

Multi-energy systems (MESs) make it possible to satisfy consumer's energy demand using multiple coupled energy infrastructures, thus increasing the reliability of the energy supply compared to separate energy systems (SESs). To accurately and efficiently assess and improve the reliability of MESs, this paper proposes a MES reliability and vulnerability assessment method using energy hub (EH) model. The energy conversion, transmission and storage in MESs are compactly and linearly described by EH model, making reliability and vulnerability assessment of MESs tractable. Indices for MES vulnerability assessment are proposed to find the key components for improving MES reliability. Multi-parametric linear programming (MPLP) with a self-adaptive critical region set is proposed to reduce the computational burden caused by iteratively solving LP problems for a large number of samples during the assessment process. The results of a case study show that the proposed reliability and vulnerability assessment method is able to effectively evaluate the energy supply reliability of different energy sectors in MES as well as find the critical component of an MES from reliability perspective to support its planning. The proposed algorithm, i.e., MPLP with a self-adaptive critical region set, can improve the computational efficiency by an order of magnitude compared to the traditional LP method. [ABSTRACT FROM AUTHOR]

To meet the newest carbon emission reduction and carbon neutrality targets, the capacity of variable renewable energy sources in China is planned to double in the next five years. A high penetration of renewable energy brings significant power system flexibility challenges, and the requirements for flexible resources become increasingly critical. Energy storage, as an effective and adaptable solution, may still be too expensive for peak shaving and renewable energy integration. A new type of business model has been proposed that uses cloud-based platforms to aggregate distributed energy storage resources to provide flexibility services to power systems and consumers. In such cloudbased platforms, storage resources can be more strategically used so that the unit cost of providing the service can be reduced. In the actual implementation of cloud-based energy storage, distributed resources have also been expanded from batteries to a variety of solutions, such as variable loads and heat storage. [ABSTRACT FROM AUTHOR]

Previous robust transmission expansion planning (RTEP) studies have rarely considered the important question of how large the uncertainty set should be, and hence, how the uncertainty budget can be determined objectively. This study addresses this issue by proposing a novel adaptive optimization method that optimizes the value of the uncertainty budget to minimize the size of the uncertainty set while considering the underlying risk for wind power generation (WPG) fluctuations residing outside of the proposed uncertainty set. As such, the proposed method ensures a good tradeoff between the robustness and costs of RTEP solutions. In addition, the proposed method optimizes investment strategies under forecasted WPG scenarios, while providing a security guarantee under extreme WPG scenarios. The variable-limit integral terms introduced by the proposed method are addressed in the solution process by applying the piecewise linearization approximation method combined with the quadratic Newton-Gregory interpolating polynomial technique, which allows the solution process to be cast as a mixed integer linear programming problem. The good performance and effectiveness of the proposed adaptive uncertainty set optimization method are verified by numerical results. [ABSTRACT FROM AUTHOR]

The coordinated multi-area economic dispatch enables optimal resource utilization in a larger spatial range. To overcome drawbacks such as heavy communication burden, convergence failure, and weak scalability of iterative coordination methods, this paper proposes a fully non-iterative multi-area coordination framework. The proposed framework is based on a novel system reduction technique termed as the condensed system representation (CSR). The CSR makes external equivalence of the dispatch problem of each area by exploiting power system operating characteristics, i.e., only a small number of generators may become marginal units and only a minority of security constraints may be active. An algorithm based on the optimality condition analysis is developed to identify the CSR by fixing non-marginal units to their output bounds, eliminating redundant security constraints, and making Norton equivalence of the internal network. With CSRs submitted by local areas, the multi-area system can be optimized without iterative information exchange. Case studies based on a 3-area 9-bus system verify the effectiveness of the CSR-based coordination framework. Larger-scale test systems are constructed to validate the computational efficiency, robustness, and scalability of the proposed method. [ABSTRACT FROM AUTHOR]

Increasing the penetration of variable generation has a substantial effect on the operational reliability of power systems. The higher level of uncertainty that stems from this variability makes it more difficult to determine whether a given operating condition will be secure or insecure. Data-driven techniques provide a promising way to identify security rules that can be embedded in economic dispatch model to keep power system operating states secure. This paper proposes using a sparse weighted oblique decision tree to learn accurate, understandable, and embeddable security rules that are linear and can be extracted as sparse matrices using a recursive algorithm. These matrix rules can then be easily embedded as security constraints in power system economic dispatch calculations using the Big-M method. Tests on several large datasets with high renewable energy penetration demonstrate the effectiveness of the proposed method. In particular, the sparse weighted oblique decision tree outperforms the state-of-art weighted oblique decision tree while keeping the security rules simple. When embedded in the economic dispatch, these rules significantly increase the percentage of secure states and reduce the average solution time. [ABSTRACT FROM AUTHOR]

Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for data-driven models under all possible training and testing scenarios drawn from an underlying distribution, and proposes an evaluation implementation based on Rademacher complexity theory. We answer critical questions for data-driven models: how much training data is required to guarantee a certain error bound, and how partial physical knowledge can be utilized to reduce the required amount of data. Different from traditional Rademacher complexity that mainly addresses classification problems, our method focuses on regression problems and can provide a tighter bound. Our results are crucial for the evaluation and application of data-driven models in power grid analysis. We demonstrate the proposed method by finding generalization error bounds for two applications, i.e., branch flow linearization and external network equivalent under different degrees of physical knowledge. Results identify how the bounds decrease with additional power grid physical knowledge or more training data. [ABSTRACT FROM AUTHOR]

The power system inertia is gradually decreasing with the growing share of variable renewable energy (VRE). This may jeopardize the frequency dynamics and challenges the secure operation of power systems. In this paper, the concept of frequency security margin is proposed to quantify the system frequency regulation ability under contingency. It is defined as the maximum power imbalance that the system can tolerate while keeping frequency within the tolerable frequency range. A frequency constrained unit commitment (FCUC) model considering frequency security margin is proposed. Firstly, the analytical formulation of system frequency nadir is derived while considering both the frequency regulation characteristics of the thermal generators and the frequency support from VRE plants. Then, the frequency security margin is analytically formulated and piecewise linearized. A novel FCUC model is proposed by incorporating linear frequency security constraints into the traditional unit commitment model. Case studies on a modified IEEE RTS-79 system and HRP-38 system are provided to verify the effectiveness of the proposed FCUC model. The impacts of VRE penetration on system frequency security are analyzed using frequency security margin. [ABSTRACT FROM AUTHOR]

The technical virtual power plant (TVPP) is a promising paradigm to facilitate the integration of distributed energy resources (DERs) while incorporating operational constraints of both DERs and networks. Due to the volatility and limited predictability of DER generation and electric loads, the output capability of the TVPP is uncertain. In this regard, this paper proposes the robust capability curve (RCC) of the TVPP, which explicitly characterizes the allowable range of the scheduled power output that is executable for the TVPP under uncertainties. Implementing the RCC can secure the scheduling of the TVPP against unexpected fluctuations of operating conditions when the TVPP participates in the transmission-level dispatch. Mathematically, the RCC is the first-stage feasible set of an adjustable robust optimization problem. An uncertainty set model incorporating the variable correlation and uncertainty budget is employed, which makes the robustness and conservatism of the RCC adjustable. A novel methodology is proposed to estimate the RCC by the convex hull of several points on its perimeter. These perimeter points are obtained by solving a series of multi scenario-optimal power flow problems with worst-case uncertainty realizations identified based on a linearized network configuration. Case studies based on the IEEE-13 test feeder validate the effectiveness of the RCC to ensure the scheduling feasibility while hedging against uncertainties. The computational efficiency of the proposed RCC estimation method is also verified based on larger-scale test systems. [ABSTRACT FROM AUTHOR]

In the upcoming era of 5G, the number of base stations, edge computing nodes and data centers is believed to be three to five times more than that of 4G. Serious challenges on the deployment and operation of 5G networks and services arise, especially on how to build and maintain battery energy storage systems for sustainable 5G power feeding at low cost for all scenarios. Although battery has long been used as a major backup power in various communications systems, current battery systems essentially are "dumb devices." In the current battery systems, the charging/discharging energy flow is continuous due to the fixed series-parallel cell topology adopted by existing battery systems. The fixed topology also causes the "bucket effect" at the system level due to the fact that it is incapable of handling cell difference in a battery system, leading to a series of system-level problems in terms of power density, energy efficiency, cycle life, reliability, and safety. All these will make it very challenging for sustainable 5G power feeding, which will further affect the cost-effective deployment and operation of 5G networks and services. Thanks to the recent breakthrough of power electronics semiconductors, such as power metal-oxide- semiconductor field-effect transistor (MOSFET), silicon carbide (SiC) and gallium nitride (GaN) with their outstanding material properties, it becomes feasible to carry out digital energy processing operations at high switching speed, high voltage, and feverish temperature. By building a new digital "grid-to-chip" power train using high switching speed power semiconductors, traditional analog battery systems can be transformed into digital battery systems through energy digitization, which will significantly facilitate feasible 5G deployment and operation. In this article, we will propose and describe the basic concept of energy digitization, the design framework of the digital battery system including key components, modeling, and the performance evaluation of the digital battery system. Results of experiments and real-world applications show the effectiveness and efficiency of digital battery system, which offer a promising disruptive approach to sustainable 5G power feeding. [ABSTRACT FROM AUTHOR]

The increasing integration of renewable energy has highlighted the importance of interchange trading in multi-area power systems (MAPS). In some existing multi-area electricity markets, the marginal-pricing (MP) mechanism is adopted. However, this mechanism is known to lose market efficiency when profit-maximizing units strategically bid to manipulate electricity prices. To elicit truthful bids from generators and guarantee the optimality of MAPS, we propose an incentive mechanism based on Vickrey-Clarke-Groves (VCG) auction. A two-stage multi-area economic dispatch model for joint energy and reserve clearing is formulated with hybrid AC/DC links. We illustrate that when settled by the MP mechanism, thermal units have incentive to strategically bid on reserve prices, thereby preventing inter-area power exchange. The VCG-based mechanism sets up the payment to a unit as other generators’ incremental costs after removing it in the market clearing process, which reveals the externality induced by this unit and thus incentivizes truthful bids. To preserve each area's autonomy, a novel decentralized algorithm based on improved optimality condition decomposition is developed. Case studies based on a 2-area 60-bus system and a 3-area 354-bus system demonstrate the effectiveness and efficiency of the proposed mechanism and method. [ABSTRACT FROM AUTHOR]

Cascading failures have become a severe threat to interconnected modern power systems. The ultrahigh complexity of the interconnected networks is the main challenge toward the understanding and management of cascading failures. In addition, high penetration of wind power integration introduces large uncertainties and further complicates the problem into a massive scenario simulation problem. This article proposes a framework that enables a fast cascading path searching under high penetration of wind power. In addition, we ease the computational burden by formulating the cascading path searching problem into a Markov chain searching problem and further use a dictionary-based technique to accelerate the calculations. In detail, we first generate massive wind generation and load scenarios. Then, we utilize the Markov search strategy to decouple the problem into a large number of DC power flow (DCPF) and DC optimal power flow (DCOPF) problems. The major time-consuming part, the DCOPF and the DCPF problems, is accelerated by the dynamic construction of a line status dictionary (LSD). The information in the LSD can significantly ease the computation burden of the following DCPF and DCOPF problems. The proposed method is proven to be effective by a case study of the IEEE RTS-79 test system and an empirical study of China's Henan Province power system. [ABSTRACT FROM AUTHOR]

Multienergy systems (MESs) are able to unlock the energy system flexibility using the coupling across multiple energy sectors. Such coupling contributes to improving the overall energy efficiency and promoting the accommodation of renewable energy. Among a wide range of literature, this article provides a perspective of network analytics on how to model, optimize, and conduct low-carbon analysis on MESs. The energy sector coupling involves different levels, for example, from a single building to nationwide. In this article, we categorize multienergy networks into two levels, that is, the district level that covers a relatively small area such as a campus or a community, where the energy conversion and utilization is the major focus, and the multiregion level that covers a relatively large area such as a big city, a province, or the whole country, where the energy transmission is the major concern. We first review the state-of-the-art multienergy networks standardized modeling approaches including: 1) energy hub (EH) model for district level energy networks; 2) network models, including power, heat, and gas steady-state and dynamic network models, for multiregion level energy networks; and 3) load models, including electricity, heat, and gas load forecasting models. Second, we explore the planning and operation methods for both district level and multiregion level energy networks. Third, we introduce a special technique named the carbon emission flow (CEF) model that is able to calculate the equivalent CO2 emission associated with the energy flows in multienergy networks. We also demonstrate how the technique can help multienergy networks planning and operation toward a low carbon society. Finally, we envision several further key research topics in the field of multienergy networks. [ABSTRACT FROM AUTHOR]

The computational efficiency of large-scale unit commitment (UC) is still a critical issue in power system and electricity market operations. To reduce the computation time of UC, relaxation-based neighborhood search (RBNS) and improved relaxation inducement (IRI) are proposed in this article. RBNS explores the neighborhood of the linear program (LP) relaxation optimal solution for a high-quality feasible solution. A new distance function, termed relaxation distance (RD), is proposed to measure the distance between the current solution and the tendency of the LP relaxation optimal solution. RBNS can substantially reduce the optimization space, and thus improve the efficiency. IRI has been developed to effectively induce binary variables towards the tendency of the relaxed solution. In contrast to a conventional relaxation inducement method, the binary variables are symmetrically and bi-directionally induced. The ratio between the inducing functions and the original objective function is optimized. IRI can induce more binary variables to integrality, and fewer binary variables need to be branched. Therefore, the size of the branch-and-bound tree can be reduced significantly. Modified IEEE-300 bus system and Polish 2746 bus system are used to demonstrate the effectiveness and performance of the proposed RBNS and IRI methods. [ABSTRACT FROM AUTHOR]

Variable renewable energy (VRE) has undergone rapid development worldwide. Currently, VRE is gradually accepted as a regular power source to participate in the power market. However, due to the fluctuating and uncertain nature, the strategic offering behaviors of VRE generation companies (GENCOs), especially in terms of available capacity withholding, are difficult to supervise and regulate. To explore the hidden danger of VRE GENCOs’ strategic offering behaviors and the sequential impacts on the market operation, this paper proposes an analysis framework, which is built based on day-ahead and real-time two-stage markets, with multiperiod coupling. The bilevel model is utilized to analyze the decision-making process of the strategic VRE GENCOs. The bilevel model is nonlinear, which is then transformed into a mixed integer linear format by applying linearization methods. An illustrative example, with a high penetration of VREs, is proposed. The strategic offering behaviors of VRE GENCOs are comprehensively analyzed. The incurred efficiency loss and payment increase are discussed with various market settings. To demonstrate the effects of the regulatory policies on the VRE strategic offering behaviors, a deviation penalty policy imposed on the deviation of VRE outputs is also proposed and tested. [ABSTRACT FROM AUTHOR]

Pipeline energy storage in district heating systems (DHSs) has great potential to improve electricity–heat coordination and promote the accommodation of renewables in power systems. However, under a decentralized operation scheme, the DHS operator may not have the motivation to utilize pipeline energy storage because it will lead to a rise in temperature and thus increase the system's overall heat loss. To address this challenge, this paper proposes a decentralized electricity–heat coordination framework by sharing part of the benefits of renewables accommodation from a power system to a DHS. A bilevel mathematical model is proposed for a policy designer to evaluate the effects of the proposed framework and determine the best sharing ratio between the two systems. An end-to-end DHS model is developed and used in the bilevel programming to consider pipeline energy storage with an explicit relationship between heat generation and demand. Finally, a case study justifies the effectiveness of the decentralized framework and proves that its performance is almost identical to the centralized power and heat dispatch framework. [ABSTRACT FROM AUTHOR]

Myriad studies have been conducted on bidding behaviors following a worldwide restructuring of the electric power market. The common theme in such studies involves idealized and theoretical economic assumptions. However, practical bidding behavior could deviate from that based on theoretical assumptions, which would undoubtedly limit the effectiveness and practicality of the prevalent market-based studies. To analyze the actual bidding behavior in power markets, this paper proposes a data-driven analysis framework for bidding behavior in which a data standardization processing method is proposed that addresses the particularities of the bidding data and provides a fundamental dataset for further market analyses. Then, an adaptive clustering method for bidding behavior is developed that applies the ${K}$ -medoids method and the Wasserstein distance measurement to extract the generators’ bidding patterns from a massive dataset. An empirical analysis of the bidding behavior is conducted on actual data from the Australian energy market. The typical bidding patterns are extracted, and the bidding behaviors are further analyzed. [ABSTRACT FROM AUTHOR]

Test systems are important tools and benchmark for power system research. Currently, there is a lack of standard test systems for modern transmission expansion planning (TEP) research, especially under high variable renewable energy (VRE) penetrations. This article describes a 38-bus test system named the HRP-38 system dedicated to TEP. “HRP” stands for high renewable penetration. The objective of establishing such a system is to provide a consistent platform for different TEP methods to be tested and compared. The test system considers high VRE penetration of more than 30% energy share, which will be an important feature in many power systems in the future. Both AC and DC candidate lines are given to provide sufficient transmission planning alternatives. The complexity of the test system is well balanced considering calculation tractability and the ability to test the performance of the TEP planning model. The network topology, generation mix and load characteristics are described in detail. Year-round time series of VRE (wind power and PV) output and load curves for each bus are provided to facilitate a detailed evaluation of the transmission grid configuration under high VRE penetration. Finally, four benchmark results with different optimization settings are provided for the HRP-38 system. [ABSTRACT FROM AUTHOR]

The nonlinearity of power flow (PF) equations challenges the analysis and optimization of power systems. Both model-based and data-driven approach was recently applied to linearize the PF equations. The data-driven approach relies heavily on the quality of the measurement data, where measurement noise may cause large modeling errors. This paper tackles the challenges of the hidden measurement noise in the data-driven PF linearization problem. We transform the problem into a regression model where the structure of the AC power flow equations is exploited. Jacobian matrix guided constraints are added to shrink the search space greatly. This regression model is formulated as three linearly constrained quadratic programming problems and is solved in an iterative manner. The effectiveness of the proposed approach is demonstrated through case studies on several IEEE standard test systems and a practical provincial system. [ABSTRACT FROM AUTHOR]

Renewable energy will become a normal energy source and will be required to participate in the market. The renewable portfolio standards (RPS) together with tradable green certificates (TGC) are considered as an appropriate market mechanism to help recover renewable energy investments. The strategic offering behaviors of renewable energy should be carefully studied, especially with the introduction of TGC. This paper proposed a market equilibrium model with joint consideration on energy and TGC. The model includes two-stages of day-ahead and real-time analysis, to formulate the complex decision processes of strategic renewable energy. The model is built in a bilevel format, with multiple-scenario settings to consider the uncertainty of renewable power output within a day. Then, the bilevel model is reformulated as a single-level nonlinear problem based on KKT theorems. Then, the nonlinearities are linearized based on the strong duality theorem and a binary expansion method. The model has been finally transformed as a mix integer linear problem. Finally, an illustrative example is proposed to analyze the strategic offering behaviors of renewable energy under various market situations. [ABSTRACT FROM AUTHOR]

The utilization rate of generation assets significantly affects the profits of generation companies. Since the daily load profiles of customers are not considered in conventional time-of-use (TOU) pricing, a fairness issue may arise because of cross subsidy. To address this issue, a block-of-use (BOU) electricity retail pricing approach is proposed, in which the price varies from block to block instead of varying from period to period. The aggregated daily load profile is horizontally divided into blocks according to the utilization rate of the generation assets. A block filling approach is proposed to allocate the customer load into different blocks. The contribution of each customer’s load profile to the generation assets can be quantified. A BOU pricing approach is proposed, in which the rate of each block consists of the fixed rate and the variable rate. The mathematical model of the proposed approach is formulated. The case study based on the actual load data verifies the effectiveness of the proposed approach and model. Compared to the TOU scheme, cross subsidy is resolved, the average rate of customers with friendly behaviors is low and that of customers with unfriendly behaviors is high in the BOU pricing. [ABSTRACT FROM AUTHOR]