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With a rapid increase in capacity, independently-invested energy storage systems (ESSs) might take up a significant share in the generation mix, and would be required to participate in the electricity market. Considering the arbitrage capability of ESSs, their behaviors would be remarkably different from those of conventional generators, impacting the market equilibrium. These impacts would vary with the type of ESSs and the generation mix. Therefore, this paper formulates a multi-period market equilibrium problem with equilibrium constraints (EPEC) to study the strategic behaviors of different types of ESSs and their impacts on the market outcomes, assuming that ESSs behave as price-makers. The EPEC model is established within a general framework, which considers the individual profit-maximization behaviors of different ESSs and generators, including thermal units, hydro units and renewable units. Interactions between different generators and the market operator are also represented. Finally, numerical studies based on a modified IEEE 57-node system with different wind generation curves are performed for illustration and validation. [ABSTRACT FROM AUTHOR]

Household energy behavior is a key factor that dictates energy consumption, efficiency and conservation. In the past, household energy behavior was typically unknown because conventional meters only recorded the total amount of energy consumed for a household over a significant period of time. The rollout of smart meters enabled real-time household energy consumption to be recorded and analyzed. This paper uses smart meter readings from more than 5000 Irish households to identify energy behavior indicators through a cross-domain feature selection and coding approach. The idea is to extract and connect customers' features from energy domain and demography domain, i.e., smart metering data and household information. Smart metering data are characterized by typical energy spectral patterns, whereas household information is encoded as the energy behavior indicator. The results show that employment status and internet usage are highly correlated with household energy behavior in Ireland because employment status and internet usage have an important effect on lifestyle, including when to work, play, and rest, and hence yield a difference in electricity use style. The proposed approach offers a simple, transparent and effective alternative to a challenging cross-domain matching problem with massive smart metering data and energy behavior indicators. [ABSTRACT FROM AUTHOR]

In liberalized electricity markets, it is essential and of great importance to develop a fair transmission cost allocation scheme that is able to reflect the “extent of use” of the transmission facilities as much as possible. In this paper, the transmission capacity is divided into four components: capacity used in normal conditions (CN), capacity reserved for contingencies (CC), capacity reserved for future use (CF) and invalid capacity (IC). CN and CC are determined by the power flow in the normal and contingency conditions of the system operation snapshot, respectively. The CF is determined by the maximum power flow in contingency conditions over a certain period (say yearly). The cost corresponding to IC would not be allocated to network users. Based on capacity usage identification, differential cost allocation methods are designed according to the functions of the transmission capacity. The cost corresponding to the capacity usage of the transmission asset under normal and contingency conditions is allocated separately. Case studies in a 3-bus system and the IEEE 30-bus system validate the good performance of the proposed structural transmission cost allocation scheme. [ABSTRACT FROM PUBLISHER]

This paper presents an approach for integrated generation and transmission maintenance scheduling model (IMS) that takes into consideration N-1 contingencies. The objective is to maximize the maintenance preference of facility owners while satisfying N-1 security and other constraints. To achieve this goal, Benders decomposition is employed to decompose the problem into a master problem and several sub-problems. A Relaxation Induced (RI) algorithm is proposed to efficiently solve the large mixed integer programming (MIP) master problem. This algorithm is based on the solution of the linear relaxed problem. It is demonstrated that the proposed algorithm can efficiently reach a near-optimal solution that is usually satisfactory. If this near-optimal solution is not acceptable, it is used as the initial solution to fast start the solution of the original IMS problem. The performance of the proposed method is demonstrated using a modified version of the IEEE 30-bus system and a model of the power system of a Chinese province. Case studies show that the proposed algorithm can improve the computational efficiency by more than an order of magnitude. [ABSTRACT FROM AUTHOR]

With the expansion and enhancement of the power transmission network, short-circuit current (SCC) has become a major concern in grid operations. While transmission switching is one effective solution to reduce the SCC without investing in additional equipment, switching lines could threaten system N-1 security. To find an efficient, and secure, yet economic switching solution, this paper proposes an optimal transmission switching model with SCC limitation and N-1 reliability. As the relationship between SCC and integer variables associated with the line states is coupled, nonlinear and difficult to explicitly express, an effective linearization method is proposed. Theoretical analysis of the linearization error is performed using an illustrative example. Case studies have shown that the linearization error is generally small and decreases rapidly with the increase of electrical distance. Based on the linearized expression for SCC, a transmission switching model with SCC limitation and N-1 reliability constraints is proposed. Furthermore, to assure the calculation accuracy for the SCC, an iterative algorithm is designed to compensate for the linearization error. Case studies on the modified IEEE 30-bus, 118-bus and practically-sized 300-bus systems validate the effectiveness of the proposed model and algorithm. The solving efficiency of the model is investigated and discussed as well. [ABSTRACT FROM PUBLISHER]

This study presents a closed‐loop coordination mode and model between generation maintenance scheduling (GMS) and long‐term security‐constrained unit commitment (SCUC) considering energy constraints and N − 1 contingencies that aims at improving the security and economy of power system operations. Given the calculation complexity, this study employs constraint transformation techniques and an efficient approach termed the relaxation induced method, which is based on the solution of the relaxed mixed‐integer programming (MIP) model. The proposed approach can quickly obtain a near‐optimal solution. If the near‐optimal solution is not acceptable for system operators, it can be used to warm‐start the solution of the original MIP problem. The modified IEEE 30‐bus test system and a provincial power system in China are employed to demonstrate the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Under conditions of imperfect competition, the issue of asymmetric information, which has not been effectively managed and settled until now, has been one of the most significant problems of the pool-based electricity market, both in theory and practice. Electricity generation companies expect to maximize their profits and control the market price by strategically bidding and their offers will necessarily deviate from the true marginal costs. These practices would result in great losses of market efficiency and incur much more payments from the consumers than actually needed. Therefore, this paper uses the analytical paradigm of economic mechanism design theory to deduce and design a customized pool-based market mechanism, which simultaneously satisfies three major properties of mechanism design theory: incentive compatibility, individual rationality and payment cost minimization. Then, several issues associated with the operational principle and implementation of this innovative mechanism are discussed and examined in detail. Finally, the results of numerical examples and case study validate the effectiveness of the proposed mechanism, which can encourage truthfulness and eliminate tacit collusion, even when there is a tight market or transmission congestion with pivotal suppliers. [ABSTRACT FROM AUTHOR]

The traditional N − 1 security assessment approaches give a limited perspective on the degree of power system security. This study proposes a novel security assessment approach using the index of post‐contingency steady‐state security distance (PCSSD). Two PCSSD models are established for contingencies causing or not causing system overloads, respectively. If the system is secure following a contingency, the PCSSD provides the shortest distance of the operating point (OP) to the post‐contingency steady‐state security region (PCSSR) boundaries, to indicate the OP's remaining security margin. If an overload condition arises after a contingency, the PCSSD provides the shortest distance of the OP to the PCSSR, to show the difficulty in eliminating power grid violations. With the index of the PCSSD, one can continuously quantify post‐contingency OPs whether they are secure or insecure and rank all possible contingencies according to their severity. The PCSSD model is a large‐scale non‐linear optimisation model which has to be calculated numerous times. A novel algorithm is proposed to improve the computational efficiency. It identifies the active boundaries of the PCSSR, and then approximates most of the PCSSDs by the corresponding ante‐contingency ones, reducing the number of optimisation problems to be solved. Case studies are performed to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper introduces a dynamic multiplier-based Lagrangian relaxation approach for the solution to multi-area economic dispatch (MAED) in a fully decentralized manner. Dynamic multipliers refer to the multipliers associated with power balance equations at tie-line buses in each area. Dynamic multipliers can be approximated as linear functions of tie-line power exports via sensitivity analysis and can serve as the equivalent supply/demand functions to neighboring areas. In contrast to the conventional static point-wise multiplier, which is unable to reflect the marginal cost change that results from variations in the power exchange level, the proposed dynamic multiplier provides each area the look-ahead capability to foresee the range of the marginal cost for power export over a range of tie-line exchange variations. In turn, this allows for a significantly faster convergence to the global optimal solution. The algorithm is also shown to be early termination friendly, which is very desirable in practice for ultra-large systems such as the State Grid of China. Numerical examples in a 6-bus system, a 3-area 354-bus IEEE system, and large test systems illustrate the benefits of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

To meet the increasingly high requirement of smart grid operations, considering AC power flow constraints in the NCUC (network-constrained unit commitment) is of great significance in terms of both security and economy. This paper proposes a decomposition method to solve NCUC with AC power flow constraints. With conic approximations of the AC power flow equations, the master problem is formulated as a MISOCP (mixed integer second-order cone programming) model. The key advantage of this model is that the active power and reactive power are co-optimised, and the transmission losses are considered. With the AC optimal power flow model, the AC feasibility of the UC result of the master problem is checked in subproblems. If infeasibility is detected, feedback constraints are generated based on the sensitivity of bus voltages to a change in the unit reactive power generation. They are then introduced into the master problem in the next iteration until all AC violations are eliminated. A 6-bus system, a modified IEEE 30-bus system and the IEEE 118-bus system are used to validate the performance of the proposed method, which provides a satisfactory solution with approximately 44-fold greater computational efficiency. [ABSTRACT FROM AUTHOR]

With the worldwide restructuring of power markets, it is more meaningful to analyze the bidding behaviors of generation companies (GENCOs). A realistic bidding behavior model can help to design better power market mechanisms. However, most existing studies usually use strong assumptions, where all GENCOs behave perfectly rationally when participating in power markets. Many GENCOs' observed bidding behaviors are proved to deviate from the simulated perfectly rational ones. Thus, it is significant to introduce individual subjective perspectives into the bidding behavior modeling to obtain more realistic models. Although several studies have been working on this topic by introducing bounded rationality, they still have some problems in modeling bounded rationality, power system operation, power market rules, etc. This paper proposes a novel bidding behavior model for GENCOs under bounded rationality with renewable energy to overcome these problems. The introduced bounded rationality features include prospect theory in the form of framing effects and the concept of fairness constraints on profit-making. A Stackelberg game model is formulated to analyze the interaction between a GENCO with renewables and a day-ahead power market. The proposed model is nonlinear, which is further transformed into a mixed-integer linear formulation by applying Karush–Kuhn–Tucker theorem and strong duality theorem. Based on a modified IEEE-30 bus system with high renewable, an illustrative example is proposed to demonstrate the effectiveness of the proposed model and analyze the bidding behaviors of bounded rational GENCOs and the corresponding effects on power markets. • A model to simulate bidding behaviors of GENCOs under bounded rationality is built. • Three bounded rationality features are integrated into the bidding model. • The nonlinear Stackelberg game model is linearized by applying a solution method. • Bidding behaviors of GENCOs with renewables under bounded rationality are analyzed. [ABSTRACT FROM AUTHOR]

Accelerated decarbonization agenda around the world requires transforming the energy industry from fossil fuel-dominated to renewable generation-dominated, which necessitates deployment of energy storage on a significantly larger scale. As a potential alternative for the most widely adopted pumped hydro storage, compressed air energy storage (CAES) is recognized as a promising component of energy sectors. Although numerous studies on CAES have contributed to the improvement of technical readiness, there are few studies on cost-effectiveness analysis to evaluate the role of CAES in urban integrated energy systems (IESs). To fill this gap, this paper proposes a planning-operation bi-level framework to evaluate the role of CAES in urban IES, and conducts an empirical study on a realistic urban IES of northern China. The cost-effectiveness of diabatic-CAES (D-CAES) and adiabatic-CAES (A-CAES) are compared in the empirical study. The results show that D-CAES can play an important role in urban IES and replace CHP as the core component to supply heat and power, reducing system overall cost and wind curtailment. The participation of D-CAES in the municipal heating sector is crucial to its superior role in urban IES. The role of A-CAES in the urban IES is relatively marginal in cases with low wind capacity and becomes more important with wind capacity increasing. • Operation characteristics of utility-scale D-CAES and A-CAES in urban IES are presented. • A planning-operation bi-level framework is proposed to evaluate CAES's role in urban IES. • Empirical study on an urban IES in northern China using real-life data. • D-CAES can be more cost-effective than A-CAES in urban IES with heating supply. [ABSTRACT FROM AUTHOR]

With large scale integration of intermittent renewable energy such as wind and solar, it is becoming a challenge to maintain the power balance by purely dispatching generation. Demand-side resources should be utilized to (1) avoid the prohibitive investment in backup generation and (2) dance with intermittent renewable generation. To reap the potential benefits from the demand side, we propose a new framework termed integrated dispatch of generation and load (IDGL). In IDGL, the load adjustment cost (LAC) is introduced to express the willingness of customers to change their consumption levels. A formulation of IDGL is proposed in which the customer responses, such as load shifting and consumption mode switching, can be explicitly modeled. We also present a new settlement mechanism in which the load adjustment costs of responsive customers are paid by non-responsive customers. The advantage of this mechanism is that customers are guided to bid according to their real LACs. Case studies based on the IEEE 30-bus system and a real-world power system in China demonstrate the effectiveness and the economic benefits of IDGL. [ABSTRACT FROM AUTHOR]

Highlights: [•] Enormous influence on ACEC caused by CE is reported. [•] Comprehensive influence of UHIE, THE and CE on ACEC is proposed. [•] A positive feedback interaction between urban microclimate and ACEC is found. [ABSTRACT FROM AUTHOR]

In response to the challenges brought on by the energy conservation based generation dispatch (ECGD) in China, a dynamic economic dispatch model that considers transmission losses (DED-TL) and a new solution framework are proposed to improve the accuracy, efficiency, and robustness of generation scheduling. Transmission losses are presented explicitly in the model so that the total energy consumption, consisting of both the generators' operation costs and also the transmission losses, can be well considered. The model is formulated as a quadratically constrained quadratic program (QCQP) problem that can be solved by commercial solvers. A penalty-based algorithm is proposed to address the virtual transmission loss problem brought on by negative nodal marginal costs. Three cases—a 6-bus system, the IEEE 30-bus system, and a 2746-bus system based on the electric energy system of Poland—are employed in numerical experiments to demonstrate the effectiveness of the proposed method. The results show that the better scheduling results in higher accuracy, and that good computational efficiency can be acquired via the proposed method. [ABSTRACT FROM PUBLISHER]

Steady‐state security region (SSR) provides a region‐wise approach to assess the steady‐state security of power systems. Based on the SSR model, we introduce the concept of 'steady‐state security distance' (SSD) to provide exact 'quantitative analysis' on the scale of security margins for system operators. Then, mathematical models of SSD are formulated. On this basis, implementation of SSD is discussed towards various uncertainties within the power system operation. As the calculation of SSD is in essence a large‐scale non‐linear optimisation process, it requires very long computation time, and thus could not be utilised in practical application. In order to enhance the efficiency of computation, a novel algorithm is proposed, which decomposes the complex optimisation process into two steps: active SSR boundary identification and partial constrained solution. The proposed algorithm remarkably cuts the scale of the optimisation model as well as the number of calculations, thus significantly reduces the computation time to meet requirements of assessment towards day ahead and hours ahead generation schedules. In the end, an IEEE‐30 bus case and a practical case on a provincial power system are both studied to testify the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

The penetration of large-scale renewable energy puts an urgent demand on increasing power grid flexibility. From the power grid perspective, transmission congestion has become one of the bottle-neck factors limiting renewable energy integration. In the tradition, transmission topology is regarded to be unadjustable, and energy storage only plays an important role in the local area where it locates. To enhance the transmission system flexibility and relieve transmission congestion, this paper proposes a network-constraint unit commitment (NCUC) model considering battery energy storage transportation (BEST) and transmission switching (TS). This model is integrated with a novel indicator based BEST model and a TS model to minimize the overall operating cost. The benefits of BEST mobility and TS flexibility are quantitatively evaluated through compared with the traditional NCUC model. Based on the difference between the overall operating cost of unit commitment (UC) and NCUC, we quantitatively evaluated the flexibility provided by BEST and TS by the overall operating cost reduction. A case study on a modified IEEE RTS-79 system is provided to validate the effectiveness of the proposed model. The results show that the BEST and TS have a synergistic effect on enhancing power grid flexibility, which reflects in this study as the power grid operating economics improvement and renewable energy curtailment reduction. Besides, though the BEST and TS bring transmission system more losses, applying them into the system could relieve transmission congestion, which makes them valuable technologies to improve power system security. • Establishing a novel indicator based BEST model for optimizing the behaviors of BEST. • Proposing a novel NCUC model considering the behaviors of BEST and TS. • Evaluating the benefits of BEST and TS in enhancing the power gird flexibility. • Finding that the BEST and TS enhance the power grid flexibility synergistically. [ABSTRACT FROM AUTHOR]

• The steady-state security region of the energy hub is defined and formulated. • An efficient approach is proposed to calculate the security region. • Critical components of the system are identified using security region analysis. • The security region is used in multi-energy systems planning. Multi-energy systems (MES) provide various types of energy services by coupling different energy sectors. Such coupling increases the efficiency and flexibility of the entire energy system, and at the same time, however, increases the dependencies of the load carrying capability among different types of load demand. In the MES planning stage, we always want to assess and compare the load carrying capability among different planning schemes, considering the N-1 or N-M contingencies and the mutual effect of different energy sectors. While in the MES operation stage, we always want to know if the load of MES can be securely supplied considering potential contingencies and how far the current operation status is from the security boundary. This paper proposes a new concept named the energy hub (EH) security region and applying the concept to depict the load carrying capability of the district MES. The concept defines a region in a hyperspace where the MES can be safely operated under steady-state operational and security constraints. We first model the district MES using the EH approach and propose the mathematical form of the EH steady-state security region systematically. Then, a vertex-based algorithm is proposed to precisely calculate the security region using a space projection technique. We further propose indices to evaluate the load carrying capability of a district MES and identify critical components. Numerical case studies are conducted on two test systems to verify the validity of the proposed method. The results show the proposed EH security region concept and calculation method provide quantitive indicators on how to compare MES planning schemes and improve the load carrying capability. [ABSTRACT FROM AUTHOR]

• A scenario-free stochastic market model dispatches the generation considering uncertainty. • A locational uncertainty marginal price prices the uncertainty of variable generation and load. • The correlation of variable generation and load is modeled in the economic dispatch and price formation. • The revenue adequacy and market equilibrium are analyzed. • The generation cost recovery is maintained considering the uncertainty. With the rapid increase of variable renewable energy sources in power systems, how to manage and price the uncertainty of renewable resources' power outputs is becoming an urgent issue. Current market designs considering the uncertainties are mainly based on the probabilistic scenario set of demand and renewable energy resources power outputs. This consideration makes market designs vulnerable to three significant challenges when put into practice. First, the accurate probability distribution of renewable generation is hard to obtain in real-time. Second, it is challenging to clear the market timely with many scenarios to guarantee accuracy. Third, generation cost recovery cannot be guaranteed for some scenarios. To overcome these challenges, this paper proposes a locational uncertainty marginal price model to price the uncertainty explicitly based on a scenario-free stochastic market-clearing model. Instead of using the probabilistic scenario set, the uncertainty of renewable energy sources and loads is modeled with distributionally-robust chance constraints. The correlation of uncertainties can be endogenously modeled in both the market-clearing and the locational uncertainty marginal price formation. Furthermore, this paper proves that generation cost recovery, revenue adequacy, and partial market equilibrium can be achieved using the locational uncertainty marginal price model. Numerical results from both the small and large systems simulations validate that the generation cost recovery is maintained no matter the generation participates in uncertainty mitigation or not. The transmission congestion surplus is also allocated appropriately among loads, renewable energy sources, and financial transmission right owners. [ABSTRACT FROM AUTHOR]

The planning of multiple energy systems requires the optimization of energy converters, energy storage devices, renewable energy resources, and energy flow pathways across multiple energy carriers. Currently, representative days are usually used to reduce calculation burden. However, planning multiple energy systems using few discontinuous representative days cannot well capture the mid-long term operation of energy storage devices. To fill this gap, this research proposes a highly efficient pure linear programming based planning approach for district multiple energy systems considering year-round operation. An energy bus framework is presented. Based on the proposed framework, the planning problem can be established as linear programming problem instead of a mixed-integer linear programming problem, which can be solved much more efficiently. It can consider year-round 8760-h continuous operation of multiple energy systems in the planning stage, which facilitates the planning of long-term energy storage devices. Case study based on a real-life multiple energy system in Beijing, China considering long-term energy storage is carried out. In the case study, the computation time of optimization is 33 s and comparisons shows that considering year-round operation in planning reduces annualized cost by 5.3% than using 24 representative days. • Planning district multiple energy system considering year-round operation. • Energy bus-based modeling framework to improve calculation efficiency in planning. • Enabling long-term energy storage planning without time series aggregation. • Real-life case study on a new subsidiary administrative center in Beijing, China. [ABSTRACT FROM AUTHOR]

Since wind turbines or photovoltaic (PV) panels are generally connected to the power grid by power electronic inverters, the power system inertia is gradually decreasing along with the growing share of renewable energy. This jeopardizes the system frequency response dynamics so that the corresponding frequency security issue is becoming the bottle-neck factor that restricts the development of high renewable energy penetration. Consequently, power system scheduling models need to incorporate frequency dynamics. The difficulty lies in how to formulate the frequency security constraints from the perspective of hourly load-generation balance since the frequency dynamics have a shorter time scale (5~30 s). Several modeling methods have been proposed based on different assumptions and simplifications. However, their accuracy is not clear. We first propose a novel method to formulate linear frequency security constraints, which considers more details of frequency response dynamics. Then, an evaluation methodology is designed to quantify the accuracy of those frequency constraints. Using this evaluation method, we compare two typical methods in recent literature with the proposed method. The results show the effectiveness and superiority of our proposed method. [ABSTRACT FROM AUTHOR]

• An efficiency-based lithium-ion battery scraping criterion for peak-shaving service. • A new battery degradation model considering scraping parameters. • Embedding the battery degradation model in energy storage operation optimization. • Benefit comparison between efficiency-based and capacity-based scraping criterion. Lithium-ion battery systems have been used in practical power systems for peak-shaving and demand response. However, a lithium-ion battery is degrading while cycling and would be scrapped when the capacity reduces to a certain experience-based threshold (e.g. 80%). The scrapping criterion could significantly affect battery lifetime and operation scheduling for grid application. Thus, such scraping criterion may not explore the maximum benefit from a new or re-used lithium-ion battery. In this paper, we propose a novel efficiency-based scrapping criterion for peak-shaving energy storage. This criterion is suitable for both new and re-used battery in grid application. Using the scraping criterion as a parameter, the maximum cycle number of battery is derived as a function of the depth of discharge, which makes the life model easy to embed in battery operation model. The results of the case study show that the operation method could maximize the benefit of peak-shaving energy storage while delaying battery degradation. Compared with the traditional 80% capacity-based scrapping criterion, our efficiency-based scrapping criterion can significantly improve the lifetime benefit of the battery by 97%. [ABSTRACT FROM AUTHOR]

• A framework for aggregating large-scale prosumers in electricity markets is proposed. • The proposed bi-level framework is linearized by duality theory and big-M method. • Cases studies based on 120,000 prosumers from Australia are conducted. • Increasing prosumers enlarges the gap between strategic and non-strategic bidding. Increased penetration of distributed energy resources is unleashing the flexibility of large-scale prosumers in deregulated markets. To explore prosumers' potential market revenues, some existing studies have focused on the strategic bidding of prosumers aggregation. A majority of those studies assume the price-taker role of the aggregator while a few studies assume the price-maker role of the aggregator. However, it remains an open question as to how the increasing number of prosumers influences the profit of a strategic aggregator. Therefore, we conduct a numerical analysis in this paper to quantify the profits of aggregating large-scale prosumers. A stochastic bi-level optimization model is proposed to depict the strategic behavior of prosumers aggregation bidding in joint energy and regulation markets. This bi-level model is transformed into a mixed-integer linear programming model by employing the Karush-Kuhn-Tucker conditions based on strong duality theory. Case studies based on 120,000 prosumers from Australia demonstrate that the strategic bidding behavior of an aggregator can lead to a 7.5% decrease in operation costs, and increasing the number of prosumers will lead to a larger gap between non-strategic and strategic behavior. [ABSTRACT FROM AUTHOR]

• A matrix modeling method for energy hub with variable energy efficiencies is proposed. • Proposed method makes the modeling of an arbitrary EH highly automated by computers. • Energy converters and energy storage are modeled under one linear modeling framework. • How to balance approximation accuracy and computational efficiency is analyzed. The modeling of multi-energy systems (MES) is the basic task of analyzing energy systems integration. The variable energy efficiencies of the energy conversion and storage components in MES introduce nonlinearity to the model and thus complicate the analysis and optimization of MES. In this paper, a standardized matrix modeling approach is proposed to automatically model MES with variable energy efficiencies based on the energy hub (EH) modeling framework. Piecewise linearization is used to approximate the variable energy efficiencies; as a result, a component with variable efficiency is equivalent to several parallel components with constant efficiencies. Splitters and concentrators are proposed as standardized components to facilitate the split and merge of energy flows imposed by piecewise linearization. The nonlinear energy conversion and storage relationship in EH can thus be further modeled under a linear modeling framework using matrices. Such matrix modeling approach makes the modeling of an arbitrary EH with nonlinear energy components highly automated by computers. The proposed modeling approach can further facilitate the operation and planning optimization of EH with variable efficiencies. Case studies are executed in MATLAB and presented to show how the nonlinear approximation accuracy and calculation efficiency can be balanced using the proposed model in the optimal operation of EH. For the optimal operation of a five-component EH with energy storage, the proposed approach reduces the approximation error from 13.7% to 0.1% with only 0.2 s computation time increase compared to the exiting constant efficiency model. [ABSTRACT FROM AUTHOR]

With the high penetration of renewable energy, such as hydro, wind and solar, curtailment is a serious issue in some regions, such as China. The key issue is lacking of flexibility, primarily due to the limited dispatchable range of thermal generators. There is an urgent need to expand the dispatchable range to promote the renewable energy accommodation. To this end, a new constraint relaxation-based day-ahead market mechanism is proposed. The trade-off between thermal units and renewable energy is achieved. Deep ramp offers and curtailment relief bids are proposed to express market participants' willingness for deep ramp and relieving curtailment. Thermal units submit their deep ramp offers to get compensation while renewable energy submits its curtailment relief bid to cut the energy price. Renewable energy is curtailed in the order of its curtailment relief bid. The model of constraint relaxation-based day-ahead market clearing procedure and model of equal curtailment without constraint relaxation are formulated, respectively. An accelerating technique combining Relaxation-based Neighborhood Search (RBNS) and Improved Relaxation Inducement (IRI) is introduced. A case study based on IEEE 118-bus system validates the effectiveness of the proposed mechanism and models and shows that the introduced accelerating technique can significantly reduce the computation time. • New constraint relaxation-based day-ahead market mechanism. • Promote the renewable energy accommodation. • Propose deep ramp offer and curtailment relief bid to achieve the trade-off. • Incent the thermal units to relax their constraints and deep ramp actively. • An accelerating technique is introduced to accelerate the solving process. [ABSTRACT FROM AUTHOR]

• A neural-network-based Lagrange multiplier selection model is formulated. • A two-stage algorithm is designed to solve the nonlinear and nonsmooth programming. • Some special designs of the neural network are proposed. • Price response feature is found to be nonlinear and temporally coupled. As a general difficult problem, the slow convergence of existing distributed demand response methods greatly hinders the reliable applications in smart grid. To overcome this problem, this paper proposes a new distributed method, namely the neural-network-based Lagrange multiplier selection (NN-LMS), to prominently reduce the iterations and avoid an oscillation. The key improvement lies in the forecast strategy of a load serving entity (LSE), who applies a specially designed neural network to capture the users' price response features. A novel Lagrange multiplier selection model, the NN-LMS model, is formulated to optimize the iterative step sizes. This complex model is then solved by a two-stage NN-LMS algorithm, containing the interval bisection method and improved sensitivity method in the first and second stages, respectively. In addition, the data selection, batch normalization and additional network are applied to boost the performance of neural networks. Case studies validate the optimality, improved convergence and numerical stability of the proposed method, and demonstrate its great potential in distributed demand response and other smart grid applications. [ABSTRACT FROM AUTHOR]

• Optimal configuration of the two representative district MESs in southern and northern China. • Up to 80% of CO 2 can be reduced and more than 40% renewable penetration can be achieved by the MES. • The average cost of reducing CO 2 rises from 491 CNY/tCO 2 to 880 CNY/tCO 2 for the Tongli system. • The cost of reducing CO 2 is higher in southern China than in northern China under the same target level. Multiple energy systems (MES) can exploit synergies among different energy sectors to optimize overall efficiency. They can accommodate renewable energy sources and reduce carbon emissions more economically than in systems where each energy sector is planned and operated separately. This paper describes the optimal planning of two real-world district MESs in China: Tongli new energy town and Tongzhou subsidiary administrative center. In addition to the conventional economy objective, two kinds of low-carbon targets, including the carbon emission target and renewable penetration target, are taken into account. The results show that investment decisions on coal-fired units, combined heat and power plants and renewable energy sources are significantly influenced by the choices of different targets. Reducing carbon emissions and increasing renewable energy integration generally work in tandem, but result in a higher planning cost. The average cost of reducing carbon emissions by 40% is 528 CNY/tCO 2 in Tongli and 327 CNY/tCO 2 in Tongzhou. Meanwhile, the average cost of achieving a 25% penetration of renewable energy is 468 CNY/MWh in Tongli and 363 CNY/MWh in Tongzhou. As expected, the cost of carbon reduction and renewable energy integration gradually rises as the targets tighten. Only a small fraction of the available renewable energy is curtailed (2.9% in Tongli and 8.3% in Tongzhou) even when the renewable penetration reaches 40% in both two systems. Finally, a sensitivity analysis illustrates the effect of planning parameters, including the carbon price, the capital cost of energy components and the fuel prices, on the planning results. [ABSTRACT FROM AUTHOR]

In the recent years, the electricity market deregulation reforms are prevalent in many countries, bringing about the problem of market power abuse. As a typical property right structure transformation approach, the vertical integration has rarely been considered and analyzed as a countermeasure towards market power abuse problem. Facing the diverging attitudes on the vertical integration around the world, the problems of whether the vertical integration should be allowed in the wholesale market still remains vague. Thus, the paper proposes a framework to analyze the decision scheme of vertically integrated companies and their corresponding impacts on the market operation. The model is built in the bilevel format, with the consideration on the particularities of electricity markets. The nonlinear model is then transformed into the linearization format. Based on the real generation mix data in Guangdong province, China, the market equilibrium with/without vertical integration is presented, combined with the analysis on the market clearing price, social welfare, and profit allocation. Finally, the policy suggestions on the vertical integration in the deregulated electricity market is proposed. • Decision schemes of vertically integrated company in power markets are formatted. • Non-vertically-integrated companies with large shares likely exercise market power. • The introduction of vertical integration will help to achieve Pareto improvements. • The mismatch between generation & retail shares is a key for market power exercise. [ABSTRACT FROM AUTHOR]

• Power system operators can understand the situation in gas system with the model. • The theory of electric analogy and reduced equivalence is transplanted to gas system. • A multi-port reduced equivalent gas network representation method is developed. • Sensitivity factors are provided for electricity-gas coordination in daily operation. Natural gas has become one of the major fuel sources for power systems in many parts of the world due to the increasing deployment of gas-fired generators. However, under the current operational procedures, the gas system operator is unable to provide detailed network data to the power system operator due to information privacy, and thus, the power system operator has very limited knowledge of the situation variation in the gas system. To better represent the interaction between electric and gas systems and increase situation awareness, a multi-port equivalent model of gas networks and electricity-to-gas sensitivity factors is proposed in this paper by transplanting the theory of electric analogy and reduced equivalence from electric circuit analysis to gas system analysis. The multi-port equivalent model enables the gas system operator to provide the reduced equivalent boundary information of the gas system without revealing detailed network data. Based on this information, electricity-to-gas sensitivity factors are calculated by power system operators. These factors indicate "where, when and how much" the status of the gas system will change as a function of changes in power generation. Both the equivalent model and sensitivity factors have explicit matrix formulations and can contribute to improving power system operators' awareness of varying conditions in the gas system. Several numerical tests are presented to prove the accuracy and robustness of the proposed models. [ABSTRACT FROM AUTHOR]

• An energy sharing scheme is developed considering the benefits for peak shaving. • A model depicting an aggregator's participation in energy and capacity markets is proposed. • Sharing contribution rate is proposed to quantify different consumers' contributions. • An incentive mechanism based on asymmetric Nash bargaining is designed. Energy sharing has gained substantial popularity in improving the controllability of distributed energy resources (DERs). The existing literature focuses on how energy sharing contributes to local power balances, but the benefits of sharing DERs for peak shaving has not been explored. In this paper, we propose a novel energy sharing scheme, in which an aggregator takes advantage of users' shared DERs for peak shaving and load balance. Based on an energy and capacity market framework, an integrated energy sharing model is formulated to incorporate the reliability charge for the aggregator. Each user is modeled as an agent making strategic investments in solar and battery storage systems while considering sharing the idle DERs. To guarantee that every user has incentive to participate in the energy sharing scheme, we design an incentive mechanism that achieves a fair benefit allocation according to users' contributions. A new index, termed sharing contribution rate (SCR), is presented that quantifies different users' contributions to both energy sharing and peak shaving. Based on the users' SCRs, an asymmetric Nash bargaining model is formulated for benefit allocation, which is solved in a decentralized manner. Case studies based on real-world datasets demonstrate that the proposed scheme can incentivize users to invest in and share DERs, thus effectively mitigating peak load. [ABSTRACT FROM AUTHOR]

• An equilibrium problem integrated with individual risk-averse features is proposed. • The conditional value-at-risk method is used for individual risk-averse evaluation. • The linearization and binary expansion method are used to solve the nonlinearity. • The model can better formulate the market with several risk-averse oligopolies. With the deregulation of world power markets, the importance of power market equilibrium analysis tools becomes more prominent. Due to the increasing uncertainty in the power markets, the influences of risk attitudes on the participants' strategic bidding behaviors and market equilibrium become more significant, which makes risk feature formulation more necessary in the market bidding decision-making process. This paper fully considers the risk-averse features of power generation companies based on the concept of the conditional value at risk and proposes a bilevel equilibrium model to practically simulate market behaviors. The mathematical model is essentially a modified equilibrium problem with equilibrium constraints. The upper-level problem of the model maximizes the profits minus the risks of each generation company, while the lower-level problem represents the market clearing processes of day-ahead markets by the independent system operator. A linearization method is proposed to transform the proposed model into a mixed-integer linear model. An illustrative example and a numerical example are performed to show the effectiveness, validity and adaptability of the proposed model. The comparisons with other equilibrium models demonstrate the better performances of the proposed method to simulate the market equilibrium with several oligopolies. The sensitivity analysis is proposed to analyze the phenomenon where different risk-averse levels and market ownership structures would result in different market equilibrium. [ABSTRACT FROM AUTHOR]