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With regard to wind farms connected to the main system via dedicated overhead transmission lines, the available wind power absorbed by the system relies on not only the power generation capacity but also the power transmission threshold. Thus, this study centers on the close relationship between environmental conditions and power transfer limits that involve the thermal limit and steady-state stability limit. Simultaneous equations representing both overhead conductors’ heat balance and power transmission operation are established. In addition, formulations of the maximum transmission power for the steady-state stability limit are derived and then expressed as functions of environmental factors and the line length. The presented approach allows one to analyze the threshold considering not only the thermal limit but also the steady-state stability limit, which could be remarkably different under certain environmental and transmission distance conditions. Then, a scheme to identify the primary limitation and obtain the corresponding threshold value is proposed. Moreover, a simple treatment of wind speed correlation is deployed because of the close distance between a wind farm and its tie-line. Case studies demonstrate that, for a certain wind speed level, the process of transmission rather than generation could become the key factor determining the threshold value. [ABSTRACT FROM AUTHOR]

The papers in this special section examine the enabling of very high penetration renewable energy integration into future power systems. Renewable energy is regarded as one of the main solutions to achieve power system sustainability. In recent years, renewable energy has been experiencing fast development. In 2016, 24.5% of the total power demand in the world was supplied by renewable energy, where 5.5% was produced by wind power and PV [item 1) in the Appendix]. It is estimated that by the end of 2017 the global wind power and PV capacity had reached about 565 GW and 335 GW respectively. Some power systems have already entered the era of high penetration of intermittent renewable energy integration. [ABSTRACT FROM PUBLISHER]

Consumption-based carbon emission accounting is able to clarify consumers’ responsibility for the carbon emissions from a power system. The responsible amount of carbon emissions for each consumer can be calculated based on the power consumption and the accordant carbon emission flow (CEF). Distribution of the CEF in the network may vary significantly under different transmission network configurations, resulting in different attributed carbon emission responsibilities of consumers. This paper describes how transmission expansion planning (TEP) and consumption-based carbon emission accounting affect each other. A novel TEP model considering the consumption-based carbon emission accounting is presented. A new index named CO 2 allocation equity coefficient (CAEC) is introduced to quantify the equity performance of the consumption-based carbon emission accounting system. As such, the requirement for different equity performances can be explicitly incorporated into the TEP model as a constraint to determine its effect on TEP. The proposed TEP model is tested on Garver’s 6-bus system and a modified IEEE 39-bus system. The results show that the methodology is able to obtain the transmission expansion planning, in general, more lines must be planned to achieve better equity performance, with more even consumption-based carbon emission, but leading to an overall increasing tendency in the annualized transmission investment cost. [ABSTRACT FROM AUTHOR]

Series resistances of overhead transmission lines (OTLs) are largely dependent on the corresponding conductor temperature. The electric current heats the conductor, and the environmental conditions affect the thermal exchange. Therefore, multiple factors related to the thermal balance codetermine the conductor temperature and resistance. However, the traditional power flow analysis neglects the thermal balance effects by treating those related factors as invariables, which may introduce some errors because of the change of the factors. Therefore, in this study, new power flow formulations, which allow for the relationship between the transmission power flow and the thermal balance, are derived. This study demonstrates the errors by applying the new formulations to calculate the power transfer limit (PTL). Moreover, the PTL of the OTL is defined either by encountering the thermal rating (TR) or the steady‐state stability limit. A case study based on an equivalent 2‐bus test system was used to illustrate the efficacy of the thermal balance that affects the PTLs of both types. In this analysis, this study also reveals the relationship between the TR and steady‐state stability limit with various physical and environmental conditions. [ABSTRACT FROM AUTHOR]

With the largest installed capacity in the world, wind power in China is experiencing a \sim 20\% curtailment during operation. The large portion of the generation capacity from inflexible combined heat and power (CHP) is the major barrier for integrating this variable power source. This paper explores opportunities for increasing the flexibility of CHP units using electrical boilers and heat storage tanks for better integration of wind power. A linear model is proposed for the centralized dispatch for integrated energy systems considering both heat and power, with detailed modeling of the charging processes of the heat storage tanks. The model balances heat and power demands in multiple areas and time periods with various energy sources, including CHP, wind power, electrical boilers, and heat storage. The impact of introducing electrical boilers and heat storage systems is examined using a simple test system with characteristics similar to those of the power systems in Northern China. Our results show that both electrical boilers and heat storage tanks can improve the flexibility of CHP units: introducing electrical boilers is more effective at reducing wind curtailment, whereas heat storage tanks save more energy in the energy system as a whole, which reflect a different heating efficiency of the two solutions. [ABSTRACT FROM PUBLISHER]

The integration of wind power requires the power system to be sufficiently flexible to accommodate its forecast errors. In the market clearing process, the scheduling of flexibility relies on the manner in which the wind power uncertainty is addressed in the unit commitment (UC) model. This paper presents a novel risk-based day-ahead unit commitment (RUC) model that considers the risks of the loss of load, wind curtailment and branch overflow caused by wind power uncertainty. These risks are formulated in detail using the probabilistic distributions of wind power probabilistic forecast and are considered in both the objective functions and the constraints. The RUC model is shown to be convex and is transformed into a mixed integer linear programming (MILP) problem using relaxation and piecewise linearization. The proposed RUC model is tested using a three-bus system and an IEEE RTS79 system with wind power integration. The results show that the model can dynamically schedule the spinning reserves and hold the transmission capacity margins according to the uncertainty of the wind power. A comparison between the results of the RUC, a deterministic UC and two scenario-based UC models shows that the risk modeling facilitates a strategic market clearing procedure with a reasonable computational expense. [ABSTRACT FROM PUBLISHER]

As a critical data input of security constraint unit commitment, bus load forecasting is currently conducted on a bus-by-bus fashion to improve the forecasting accuracy in most provinces in China. On such substation level forecasting, data quality is much worse than the aggregated power demand for the whole system, and identifying and restoring the inaccurate measurement and abnormal disturbance to retrieve the historical trend of load is urged to improve the forecasting accuracy. In this paper, a two-stage identification and restoration method is presented. The typical patterns of inaccurate measurement and abnormal disturbance are detected in the first stage based on statistical criteria independent with normal distribution. Historical trend is further retrieved in the second stage using frequency domain decomposition, and a typical daily curve is generated to compare with the data measurements. The deviations of the data measurements from the typical daily curve obey normal distribution and are used as criteria in the second stage. The effectiveness of the proposed methodology has been confirmed by examples in real bus load forecasting systems in this paper. [ABSTRACT FROM PUBLISHER]

The integration of wind power requires additional operating reserves to cope with the uncertainty in power system operation. Previous research shows that the uncertainty of the wind power forecast varies with the level of its output. Therefore, allocating reserves dynamically according to the specific distribution of the wind power forecast would benefit system scheduling. This paper presents a statistical model to formulate the conditional distribution of forecast error for multiple wind farms using copula theory. The proposed model is tested using a set of synchronous data of wind power and its day-ahead forecast. It is then utilized in a stochastic unit commitment model to simulate the day-ahead and real-time scheduling of the modified IEEE RTS-79 system integrating wind power. The results show that scheduling reserves dynamically according to the modeled conditional forecast error reduces the probability of reserve deficiency while maintaining the same level of operating costs. [ABSTRACT FROM PUBLISHER]

Abstract: In China, the electricity sector consumes approximately 50% of the coal and emits 40% of the CO2 from fossil fuel combustion. The unbalanced spatial distribution between energy resources and demands and the remarkable differences in power-generation capabilities among regions are important factors that impede decarbonization of China's electricity sector. Utilization of the abundant low-carbon energy resources in the central and western regions is restricted by limited local demand. Energy demand in these regions accounts for approximately 26% of the entire nation's demand. By comparison, the regions have more than 45% of the energy resources. However, long-distance energy delivery incurs considerable losses. At present, approximately 80% of inter-regional energy delivery uses primary coal transport and 20% travels by secondary electricity transmission. The Chinese government is planning to build an ambitious inter-regional transmission grid for energy delivery. We demonstrate that this plan would significantly change the current delivery patterns and improve delivery efficiency. Approximately 40% of inter-regional energy delivery would travel by secondary electricity transmission and a 25% improvement in the delivery efficiency of the entire system is expected. Therefore, utilization of low-carbon energy resources would be promoted and overall carbon emission would be reduced. Using a fine-grained electricity dispatch model to simulate and optimize the operation of the power system, the carbon emission mitigation potential is quantitatively assessed based on real planning data. The results indicate a significant 10% reduction in CO2 emissions in 2030, amounting to 0.49Gt. This reduction should be included as an important component for the sector's low-carbon budget. Finally, we assess the potential for further reductions in carbon emissions by making modifications to the planned transmission grid. [Copyright &y& Elsevier]

In a carbon-constrained world, CO2 emissions will become a new concern in power system dispatch. Meanwhile, carbon capture power plants (CCPPs), which are a critical low-carbon power generation option, will have a significant impact on power system operation and dispatch. This paper presents research on low-carbon power system dispatch (LCPSD) incorporating CCPPs. The operating mechanism of CCPPs is investigated first. Then, the operating characteristics of CCPPs in power system dispatch are analyzed, including feasible power output limits, ramping rates and relationships between the power outputs and the carbon emissions. A comprehensive LCPSD model is formulated, in which the carbon emissions of power plants are treated as a new set of decision variables, and low-carbon-related cost terms are considered. The dispatch features of CCPPs are elaborately formulated and incorporated into the LCPSD model. The effectiveness and the validity of the proposed LCPSD mode and model are demonstrated using numerical examples based on an IEEE 118-bus tested system. [ABSTRACT FROM PUBLISHER]

Abstract: In a smart grid framework, relations between the system operator (SO) and terminal consumers will become interactive and demand-side response capacities can be integrated as dispatch-able resources. This paper proposes a systematic analysis on demand-side response mechanism in smart grid. A multi-agent (MA) system is established to describe interactive relations between the SO and different kinds of consumers. On this basis, a novel mechanism is proposed to reflect the process of interactive response, which consists of three schemes: data clustering and release scheme, demand-side interactive response capability (DIRC) submission scheme, and submission correction scheme. Then, a standard data format is defined to formulate the submission of DIRC from basic consumers and a fuzzy-C-mean clustering method is implemented to generate and release typical interactive response modes (IRM) for different kinds of consumers. Moreover, a correction method based on similarity identification is developed to modify submission of DIRC by taking into account deviations between historical submissions and real performances. Finally, a simulation case verifies the effectiveness and rationality of the proposed mechanism, models and methods. [Copyright &y& Elsevier]

Abstract: Climate change poses huge challenges to the sustainable development of human society. As a major CO2 emission source, decarbonization of power sector is fundamental for CO2 emission abatement. Therefore, considering the “carbon lock-in” effects, it’s critical to formulate an appropriate roadmap for low-carbon generation technologies. In this paper, key low-carbon technology solutions are firstly identified according to their developing prospects and the fundamental realities of China’s power sector. Then, costs, reduction effects and potentials for the key technology options are evaluated. On this basis, typical scenarios are selected and a scenario set is established which identifies and incorporates the key low carbon factors, and a multi-scenario analysis is implemented to China’s power sector based on a comprehensive power mix planning model. Then, contributions of CO2 reduction among the key technology solutions are revealed. Prospect for CO2 emission reduction is discussed, which informs the possible emission trajectories towards 2030. Finally, low-carbon technology roadmaps under specific scenarios are elaborated, which implies corresponding optimal evolution of power generation mix. [Copyright &y& Elsevier]

Abstract: A novel model and algorithm for analyzing the uncertainties in electricity market is proposed in this paper. In this model, bidding decision is formulated as a probabilistic model that takes into account the decision-maker''s willingness to bid, risk preferences, the fluctuation of fuel-price, etc. At the same time, generating unit''s uncertain output model is considered by its forced outage rate (FOR). Based on the model, the uncertainty of market price is then analyzed. Taking the analytical results into consideration, not only the reliability of the power system can be conventionally analyzed, but also the possible distribution of market prices can be easily obtained. The probability distribution of market prices can be further used to calculate the expected output and the sales income of generating unit in the market. Based on these results, it is also possible to evaluate the risk involved by generating units. A simple system with four generating units is used to illustrate the proposed algorithm. The proposed algorithm and the modeling technique are expected to helpful to the market participants in making their economic decisions. [Copyright &y& Elsevier]

With structural changes to electricity industry in recent years, there is an increasing emphasis on short-term load forecasting (STLF). STLF is the essential part of power system planning and operation. In deregulated environment, STLF greatly affects system marginal price. Therefore many kinds of models for STLF have been proposed in recent 30 years. However, an individual forecasting model cannot work well in every possible condition. To overcome this problem, an integrated model, based on the idea of simulative forecasting, which can synthesize individual forecasting model results, is proposed in this paper. In practice, STLF is always affected by a variety of nonlinear factors. Since it is difficult to identify the nonlinearity by conventional methods, a novel approach is proposed which is able to deal with most factors affecting power load. The major idea in the approach is to define a mapping function for each factor. Finally, an algorithm for optimizing mapping functions is presented. Case studies show that it is a successful solution to improve the accuracy of STLF. [Copyright &y& Elsevier]

The primary objective of this paper is to report a new mathematical approach named sequence operation theory (SOT) to handle the difficulty and complexity of power system reliability evaluation (PSRE) that arises from integrated resource planning or under deregulation environment. Enlighten by traditional discrete convolution, four type of discrete sequence operation (DSO) named addition-type-convolution, subtraction-type-convolution, AND-type-product and OR-type-product, are proposed in this paper. A special type of discrete sequence named probabilistic sequence (PS) has been studied in detail. In order to put SOT into practice, the definition of expected value of PS is presented according to Probability Theory. Moreover, some physical concepts of DSO between two PSs have been discussed. As a exact mathematical theory, all the properties of SOT are proved.SOT is a new general mathematical theory and wide application in many fields could be expected. A new PSRE method based on the theory of SOT is briefly reported in this paper. When dealing with traditional PSRE problems, such as IEEE Reliability Test System, the numeric example shows that the result of our approach is almost the same as that of traditional one. However, the new method can solve the newly emerging problem of PSRE in integrated resource planning or under deregulation environment, where the whole demand would be classified into several types and each type can be given a certain reliability index. [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]

Discrete convolution (DC) is a generally accepted approach for the probabilistic analysis such as reliability assessment and probabilistic load flow. However, it has a strong precondition that the stochastic variables being convolved must be independent, which may not be fully satisfied in all cases. Using copula functions, this letter derives the formulation of DC for dependent variables. The performance of the proposed dependent discrete convolution (DDC) is illustrated using reliability assessment involving wind power. The result shows that the DDC inherits the efficient and reliable performance of DC, indicating a promising potential for practical applications. [ABSTRACT FROM PUBLISHER]

The increasing complexity of power systems, particularly the high renewable energy penetration, raises the necessity of incorporating detailed power system operation models into long-term planning studies. The classic short-term operation model, i.e., network-constrained unit commitment (NCUC), involves many binary variables and introduces computational challenges when applied to long-term planning optimizations. A high-efficiency and simplified NCUC model is required to incorporate operational flexibility in power system planning studies. This paper proposes a linearized NCUC formulation that has a high calculation performance and minor approximation errors compared to the full NCUC model. The proposed model combines the dispatch-only (DO) operation model and clustered unit commitment (CUC) model by introducing linking constraints between them such that the overall model guarantees both the transmission security constraints that are formulated in the DO model and the start-up/shut-down constraints of generating units that are formulated in the CUC model. A case study of a modified IEEE RTS-79 system is provided to demonstrate the validation and efficiency of the proposed simplified NCUC model as well as its effectiveness for power system planning studies. [ABSTRACT FROM AUTHOR]

A foreword to the March 15, 2015 issue of "IEEE Transactions on Power Systems" is presented.

Multi-energy systems can provide a constant level of service to end-use energy demands, while deriving delivered energy from a variety of primary/secondary energy sources. This fuel-switching capability can be used to reduce operating expenses, reduce environmental impacts, improve flexibility to accommodate renewable energy, and improve reliability. This paper presents four frameworks for incentivizing energy hub equipment investments for low-carbon operation targets. These frameworks vary in the measures taken to achieve low-carbon operation (explicit constraint versus carbon pricing) and in the relationship between the hub builder and operator (cooperative versus uncoordinated). The underlying energy hub model upon which these frameworks are built is an enhanced greenfield model, introducing “energy buses” to reduce dimensionality. A case study is conducted for a campus being designed in Beijing, and results from each framework are compared to illustrate their relative costs. When the operator cannot be trusted to cooperate in controlling emissions, the system must be “overbuilt” with more expensive equipment to ensure emissions target are met. A taxation-based approach increases overall costs at moderate emissions targets, but this effect decreases at aggressive targets. This paper also compares the cost of less efficient institutional frameworks with the most efficient approach, i.e., cooperation between builder and operator with constraints on emissions. [ABSTRACT FROM AUTHOR]

Composite power system reliability evaluation is computationally time-consuming because the optimal power flow (OPF) with the least load curtailment is calculated for a large number of samples. Most current studies of probabilistic simulation methods focus on sampling techniques to improve the sampling efficiency and decrease the calculations of the OPF. This paper proposes a fast reliability evaluation method that accelerates the calculation of the OPF using multi-parametric linear programming (MPLP). In this paper, the sampled transmission line statuses are considered by the transmission line status dictionary (TLSD). We match the line status of each sample with the scenarios in the TLSD. For matching samples, the generation status and the sampled load are treated as MPLP parameters of the DC-OPF evaluation model. A dynamic learning algorithm is applied to solve the MPLP problem. Case studies are conducted on both IEEE Reliability Test Systems and a provincial power system in China. The results show that the proposed method improves the evaluation efficiency by 23–30 times compared with the normal DC-OPF-based model. [ABSTRACT FROM AUTHOR]

In the power system stochastic unit commitment (SUC) model, the uncertainty and variability of the renewable energy generation, such as wind power, are usually expressed by multiple scenarios. The performance of SUC is driven by how well the selected scenarios represent the stochastic nature, since too few scenarios may lose or distort the uncertainty or variability characteristics, while too many scenarios usually cause computational difficulty. This paper proposes a novel scenario representation method referred to as scenario mapping technology (SMT), which is able to compact large amount of scenarios while preserving the uncertainty and variability features of wind power as much as possible. Specifically, SMT proposes to combine similar wind power generation into a state at each period and contains the possible multiple transitions linking the states in adjacent periods. A novel two-stage SUC model is proposed based on SMT. The numerical results demonstrate a solid improvement of the performance for the SMT-based SUC model on both cost-efficiency and calculation efficiency, compared with traditional two-stage SUC model based on the scenario reduction technique. [ABSTRACT FROM AUTHOR]

The integration of high penetration of renewable energy brings greater uncertainties for the operation of future power systems due to its intermittency and lack of predictability. The uncertainties brought by wide scale renewables might have dependencies with each other because their outputs are mainly influenced by weather. However, an analysis of such uncertainties with complex dependencies faces the “curse of dimensionality”. This challenges the power system uncertainty analysis in probabilistic forecasting, power system operation optimization, and power system planning. This paper proposes an efficient approach that is able to handle high-dimensional dependencies. The approach uses the high-dimensional Copula theory and discrete convolution method to conduct a high-dimensional dependent discrete convolution (DDC) calculation. A recursive algorithm is proposed to decompose the computation of DDC into multiple convolutions between each pair of stochastic variables so that the “curse of dimensionality” is solved. The computational complexity of the proposed method is linear with respect to the number of dimensions and guarantees computational efficiency. Finally, illustrative examples of power system reserve requirement evaluation and wind power capacity credit assessment analysis are used to verify the effectiveness and superiority of the proposed approach. [ABSTRACT FROM PUBLISHER]

With the largest installed capacity in the world, wind power in China is experiencing a ∼20% curtailment. The inflexible combined heat and power (CHP) has been recognized as the major barrier for integrating the wind source. The approach to reconcile the conflict between inflexible CHP units and variable wind power in Chinese energy system is yet unclear. This paper explores the technical and economic feasibility of deploying the heat storage tanks and electric boilers under typical power grids and practical operational regulations. A mixed integer linear optimization model is proposed to simulate an integrated power and heating energy systems, including a CHP model capable of accounting for the commitment decisions and nonconvex energy generation constraints. The model is applied to simulate a regional energy system (Jing–Jin–Tang) covering 100-million population, with hourly resolution over a year, incorporating actual data, and operational regulations. The results project an accelerating increase in wind curtailment rate at elevated wind penetration. Investment for wind breaks even at 14% wind penetration. At such penetration, the electric boiler (with heat storage) is effective in reducing wind curtailment. The investment in electric boilers is justified on a social economic basis, but the revenues for different stakeholders are not distributed evenly. [ABSTRACT FROM AUTHOR]

A new challenge has arisen in power generation scheduling recently, as the rapid increase in the number of gas-fired units has made power systems more vulnerable to failures in natural gas networks. The large-scale integration of wind power further exacerbates the problem because gas-fired units are usually scheduled to catch up wind power uncertainty and thus lead to great variations in the state of gas network. To meet this challenge, it is necessary to commit and dispatch the gas-fired units considering both wind uncertainty and natural gas network security. However, the dynamic characteristics of gas flow are remarkably slower than those of power flow, which should be appropriately modeled to explore its benefits for power system operation. Because directly applying partial differential equations overly complicates the already complicated generation scheduling problem, we address this problem by proposing an approximated transient matrix-form gas flow model. A two-stage robust generation scheduling model is then proposed considering the dynamic security constraints of gas networks and the wind power uncertainty. Moreover, we successfully avoid the nonlinearity of gas flow constraints by developing a new solution methodology. Finally, an illustrative case is presented to demonstrate the effect of gas network dynamics in generation scheduling. [ABSTRACT FROM AUTHOR]

In some highly populated areas of the planet, air quality is an issue. Since power plants in those areas significantly contribute to air pollution due to SO 2 and NO x emissions, it is appropriate to operate such power plants enforcing admissible emission limits. Since emissions directly impact the air quality index, which is highly uncertain 24 h in advance, admissible emission limits should be modeled as stochastic parameters.To represent the stochastic nature of such admissible emission limits, we propose a two-stage stochastic programming model for the commitment of power plants. The first stage represents the day-ahead scheduling, and the second stage represents the real-time power system operation under different weather and thus admissible emission limit conditions. We show the effect of stochastic emission limits on power system operations using a simple example and a 118-node case study. [ABSTRACT FROM AUTHOR]

In the future power system with high penetration of renewables, renewable energy is expected to undertake part of the responsibility for frequency regulation, just as the conventional generators. Wind power and battery storage are complementary in accuracy and durability when providing frequency regulation. Therefore, it would be profitable to combine wind power and battery storage as a physically connected entity or a virtual power plant to provide both energy and frequency regulation in the markets. This paper proposes a real-time cooperation scheme to exploit their complementary characteristics and an optimal bidding strategy for them in joint energy and regulation markets, considering battery cycle life. The proposed cooperation scheme is adopted in a real-time battery operating simulation and then incorporated into the optimal bidding model. The scheme could improve the wind regulation performance score and allow for more regulation bids without affecting the battery life, thus significantly increasing the overall revenue. The validity of the proposed scheme and strategy are proved by the case study. [ABSTRACT FROM PUBLISHER]

Linearized power flow models are of great interest in power system studies such as contingency analyses and reliability assessments, especially for large-scale systems. One of the most popular models—the classical DC power flow model—is widely used and praised for its state independence, robustness, and computational efficiency. Despite its advantages, however, the DC power flow model fails to consider reactive power or bus voltage magnitude. This paper closes this gap by proposing a decoupled linearized power flow (DLPF) model with respect to voltage magnitude and phase angle. The model is state independent but is distinguished by its high accuracy in voltage magnitude. Moreover, this paper presents an in-depth analysis of the DLPF model with the purpose of accelerating its computation speed, leading to the fast DLPF (FDLPF) model. The approximation that is applied to obtain the FDLPF model from the DLPF model is justified by a theoretical derivation and numerical tests. The proposed methods are provably accurate and robust for several cases, including radial distribution systems, meshed large-scale transmission systems and ill-conditioned systems. Finally, expressions for sensitivity with regard to MW flow and bus voltage are provided as a potential application. [ABSTRACT FROM PUBLISHER]

With the rapidly increasing penetration of wind power, wind producers are becoming increasingly responsible for the deviation of the wind power output from the forecast. Such uncertainty results in revenue losses to the wind power producers (WPPs) due to penalties in ex-post imbalance settlements. This paper explores the opportunities available for WPPs if they can purchase or schedule some reserves to offset part of their deviation rather than being fully penalized in the real-time market. The revenue for WPPs under such mechanism is modeled. The optimal strategy for managing the uncertainty of wind power by purchasing reserves to maximize the WPP's revenue is analytically derived with rigorous optimality conditions. The amount of energy and reserves that should be bid in the market is explicitly quantified by the probabilistic forecast and the prices of the energy and reserves. A case study using the price data from ERCOT and wind power data from NREL is performed to verify the effectiveness of the derived optimal bidding strategy and the benefits of reserve purchasing. Additionally, the proposed bidding strategy can also reduce the risk of variations on WPP's revenue. [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]

In highly polluted regions, it might be necessary to operate power plants enforcing appropriate emission limits to help ensuring air quality. If the air quality is low and pollutants are difficult to diffuse, few additional emissions should be allowed to avoid further deteriorating the air quality. Wind power helps reducing pollution levels as it substitutes polluting thermal production. However, wind and air pollution are generally anti‐correlated. Storage can be used to shift wind power production from hours when higher pollution levels are allowed to hours when lower pollution levels are allowed to help meeting emission constraints at critical hours. Storage availability also achieves a comparatively lower production cost for the system as a whole. To represent the stochastic nature of wind power production and weather‐dependent emission limits, the authors propose a two‐stage stochastic programming model to analyse the trade‐off emission versus cost in an electric energy system with storage facilities. The first stage represents the day‐ahead scheduling, while the second one represents the real‐time operation under different scenarios. The authors analyse the impact of emission limits and/or storage on generation scheduling by comparing different models using an illustrative example and a case study based on the IEEE 118‐node system. [ABSTRACT FROM AUTHOR]

Smart meters play vital roles in the aspects of the management and operation of smart grids such as demand response, energy efficiency improvement, and electricity pricing. Massive amounts of data are being collected owing to the popularity of smart meters. Two main issues should be addressed in this context. One is the communication and storage of big data from smart meters at reduced cost. The other is the effective extraction of useful information from this massive dataset. In this paper, the K-SVD sparse representation technique, which includes two phases (dictionary learning and sparse coding), is used to decompose load profiles into linear combinations of several partial usage patterns (PUPs), which allows the smart meter data to be compressed and hidden electricity consumption patterns to be extracted. Then, a linear support vector machine (SVM) based method is used to classify the load profiles into two groups, residential customers and small and medium-sized enterprises (SMEs), based on the extracted patterns. Comprehensive comparisons with the results of k-means clustering, the discrete wavelet transform (DWT), principal component analysis (PCA), and piecewise aggregate approximation (PAA) are conducted on real datasets in Ireland. The results show that our proposed technique outperforms these methods in both compression ratio and classification accuracy. [ABSTRACT FROM AUTHOR]

Energy storage is extensively recognized as a significant potential resource for balancing generation and load in future power systems. Although small residential and commercial consumers of electrical energy can now purchase energy storage systems, many factors, such as cost, policy and control efficiency, limit the spread of distributed energy storage (DES). This paper proposes a new type of DES—cloud energy storage (CES)—that is capable of providing energy storage services at a substantially lower cost. This grid-based storage service enables ubiquitous and on-demand access to a shared pool of grid-scale energy storage resources. It provides users the ability to store and withdraw electrical energy to and from centralized batteries. This paper describes the concept of CES and the control and communication technologies that are required for its implementation and its operating mechanism, as well as its business model. Simulation results that are based on actual power system operating data demonstrate the feasibility and economic benefit of CES. [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]

The increasing frequency of the occurrence of high impact low probability (HILP) disruptive events has posed huge threats to the power system. Therefore, power system resilience improvement has drawn world-wide attention. Electric buses (EB) are equipped with a large capacity of batteries which could provide sufficient energy and capacity values to run islanded micro-grids (MG) for hours, with a particular focus on V2G, which will deliver system resilience during extreme operational conditions. In this paper, an innovative reciprocal transport-power system coordination scheme is proposed to simultaneously mitigate the adverse impacts on both systems. By using this economic incentive-free approach, EBs are endogenously motivated to provide resilience-oriented V2G service to the power system, while increasing the transport service survivability during HILP disruptive events. Additionally, a novel EB dispatch rescheduling method considering the trade-off of both the requirement of energy supply security and transport service fulfilment is proposed. By using this method, the essential load curtailment of local MGs can be further alleviated, while the impacts of reduced charging service on transport service can also be further mitigated. Through a series of case studies, we illustrate that EBs have significant potential to provide resilience enhancement to the urban energy system based on appropriate dispatch strategies. Meanwhile, the interests of the power system and the transport system can be well reconciled by using the proposed approach during HILP disruptive events. • Present an algorithm for resilient operation of power system and transport system. • Propose a reciprocal coordination scheme reconciling the interests of both systems. • Investigate the values of strategic EB dispatch rescheduling in improving resilience. • Quantify energy values and capacity values of EBs in enhancing system resilience. [ABSTRACT FROM AUTHOR]

Vanadium redox flow batteries (VRBs) are a promising solution to facilitate the expansion of intermittent renewables. The efficiency of VRBs is dynamically dependent on the instantaneous operating states, including the output power level and state of charge (SOC). In addition, VRB capacity fades over long‐term cycling and thus might limit the market revenue. Modelling these two factors would assist deciding better operating strategy, enhance and more precisely evaluate the economics of VRBs. This study establishes an operation optimisation model that integrates the characteristics of the dynamic efficiency performance and the capacity loss of VRBs. First, the dynamic efficiency functions of the operating states are transformed into VRB power functions using a fitting technique to be compatible with the operation optimisation model and reduce the computational complexity. The VRB power functions are then embedded into the operation optimisation model, which decides the optimal charge–discharge schedule of VRBs to maximise the revenue. The capacity loss model is also implemented to estimate the revenue decrease caused by capacity loss, which could help decide the preventive measures and capacity restoration from an economic perspective. The validity of the proposed model is demonstrated by case study results based on the market prices of ERCOT in 2014. [ABSTRACT FROM AUTHOR]

The rapid development of wind power has led to an increased demand for spinning reserve in power systems today. However, one of the most severe challenges to China's power systems is the mismatch between wind power installation capacity and the capability for supplying spinning reserve within each independently operated provincial power system. Coordinating the spinning reserve across multiple areas would providentially improve the accommodation of wind power. This paper proposes a game-theoretical model for spinning reserve trading between provincial systems that treat spinning reserve as a commodity. Based on the incomplete information, the trading price is calculated by satisfying the Bayesian Nash equilibrium, and then the trading quantity is determined. This ensures that both the buyer and the seller are able to maximize their expected profit. Case studies are performed using a 2-bus interconnected system and a 3-area IEEE RTS system. The results show that the proposed model is valid and effective. [ABSTRACT FROM AUTHOR]

In response to the computational challenges produced by the integrated dispatch of generation and load (IDGL), this paper proposes a novel and efficient decomposition method. The IDGL is formulated using the mixed-integer quadratic constrained programming (MIQCP) method. To efficiently solve this complex optimization problem, the nodal equivalent load shifting bidding curve (NELSBC) is proposed to represent the aggregated response characteristics of customers at a node. The IDGL is subsequently decomposed into a two-level optimization problem. At the upper level, grid operators optimize load shifting schedules based on the NELSBC of each node. Transmission losses are explicitly incorporated into the model to coordinate them with generating costs and load shifting costs. At the bottom level, customer load adjustments are optimized at individual nodes given the nodal load shifting requirement imposed by the grid operators. The key advantage of the proposed method is that the load shifting among different nodes can be coordinated via NELSBCs without iterations. The proposed decomposition method significantly improves the efficiency of the IDGL. Parallel computing techniques are utilized to accelerate the computations. Using numerical studies of IEEE 30-bus, 118-bus, and practically sized 300-bus systems, this study demonstrates that accurate and efficient IDGL scheduling results, which consider the nonlinear impact of transmission losses, can be achieved. [ABSTRACT FROM PUBLISHER]

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]

Virtual power plants (VPPs) have become an important technological means for large-scale distributed energy resources to participate in the operation of power systems and electricity markets. However, the operation of VPPs is challenged by stochastic resource characteristics, complex control features, heterogeneous information structures, and strategic game behaviors among stakeholders. To clarify the key problems and solutions to these challenges, this article describes the resource coordination problems and multidimensional interaction mechanism, and it elaborates the overall decision-making process of VPPs. It also discusses different specific operational stages that VPPs should attach importance to from three separate perspectives: energy, communication and the market. From each perspective, every section first analyzes the motivation of decision-making, then analyzes the complexity of the problem models, and summarizes the different modeling methods and solving techniques, thus completing a comprehensive review of VPP operation. Furthermore, the article adopts an interdisciplinary approach, utilizing a literature review and technical statistics to capture the multifaceted contributions of decision-making to VPP operations. It delves into the evolving trends of decision-making technology, analyzed from the coupling cyber-physical-social perspective. Finally, the future trajectory of research issues is deliberated. • Revealed the interaction mechanism of VPP from cyber-physical-social multiple perspectives. • Reviewed the operation problems and techniques of VPP from interdisciplinary concept. • Summarized the intrinsic change and analyzed the evolution trajectory of VPP operation techniques. [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]

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]

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]

In response to the challenges brought by uncertain bus load and volatile wind power to power system security, this paper presents a novel unit commitment formulation based on interval number optimization to improve the security as well as economy of power system operation. By using full-scenario analysis, the worst-case impact of volatile node injection on unit commitment is acquired, so that the proposed model can always provide a secure and economical unit commitment result to the operators. Scenarios generation and reduction method based on interval linear programming theory are used to accelerate the solution procedure without loss of optimality. Benders decomposition is also implemented to reduce the complexity of this large-scale interval mixed integer linear programming, and prove the rationality and rigor of our proposed method. The numerical results indicate better secure and economical features of the proposed method comparing with the traditional one. [ABSTRACT FROM PUBLISHER]

Short-term load forecasting (STLF) is the basis of power system planning and operation. With regard to the fast-growing load in China, a novel two-stage hybrid forecasting method is proposed in this paper. In the first stage, daily load is forecasted by time-series methods; in the second stage, the deviation caused by time-series methods is forecasted considering the impact of relative factors, and then is added to the result of the first stage. Different from other conventional methods, this paper does an in-depth analysis on the impact of relative factors on the deviation between actual load and the forecasting result of traditional time-series methods. On the basis of this analysis, an adaptive algorithm is proposed to perform the second stage which can be used to choose the most appropriate algorithm among linear regression, quadratic programming, and support vector machine (SVM) according to the characteristic of historical data. These ideas make the forecasting procedure more accurate, adaptive, and effective, comparing with SVM and other prevalent methods. The effectiveness has been demonstrated by the experiments and practical application in China. [ABSTRACT FROM PUBLISHER]

Abstract: Transmission pricing scheme is a key component in the infrastructure of power market, and pool is an indispensable pattern of market organization; meanwhile, pay-as-bid (PAB) serves as a main option to determine market prices in pool. In this paper, a novel transmission pricing scheme is proposed for pool power market based on PAB. The new scheme is developed by utilizing point-to-point (PTP) tariff and introducing an approach of transaction pair matching (TPM). The model and procedure of the new scheme are presented in detail. Apart from the advantages of existing transmission pricing schemes, such as ensuing open, fair and non-discriminatory access, proper recovery for investment as well as transparency, the new scheme provides economic signals to promote the maximum use of the existing transmission network, encourages appropriate bidding behaviors in pool, and helps to reduce the possibility of the enforcement of market power and the appearing of price spikes; thus improves market operation efficiency and trading effects. In order to testify the effectiveness of the proposed scheme, a case based on IEEE 30-bus system is studied. [Copyright &y& Elsevier]