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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]

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]

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]

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]

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: It is very important for electric utility to determine dominant sectors which have more impacts on electricity consumption in national economy system. In this paper, an input-output perspective and methodology is proposed to handle this issue. The input-output table of electricity demand (IOTED) is put forward based on the input-output table of national economy (IOTNE). The relevancy of electricity demand in various sectors is revealed by means of electricity consumption chains (ECCs), which are key components in the IOTED. Besides, a new concept, electricity demand multiplier (EDM), is presented to identify dominant sectors imposing great impacts on electricity demand quantitatively. In order to testify the effectiveness of the proposed methodology, a case based on provincial economy system in China is studied. Dominant sectors are identified and discussed. [Copyright &y& Elsevier]

The high penetration of renewable energy will substantially change the power system operation. Traditionally, the annual operation of a power system can be represented by some typical operation modes and acts as the basis for the power-system-related analysis. The introduction of highly penetrated renewable energy will make the power system operation mode highly diversified and variable. These modes may not follow traditional empirical patterns. In this paper, we propose a data-driven method based on high-dimensional power system operation data (including power flow, unit generation, and load demand) to identify the pattern of the operation modes and analyze the impact of high renewable penetration. Specifically, the proposed data-driven method is composed of simulation, preprocessing, clustering, dimension reduction, and visualization with the aim to provide an intuitive understanding of the operation mode variety under high renewable penetration. In addition, several indices are introduced to quantify the space dispersion, time variation, and seasonal consistency of operation modes. A case study on actual Qinghai provincial power system in China validates the effectiveness of the proposed data-driven method and indicates that the dispersion and time variation of operation mode will significantly increase in the beginning and then saturate with the increase in renewable penetration level. The operation mode is also less correlated with seasons in renewable energy dominated power system. [ABSTRACT FROM AUTHOR]

• The concepts of probabilistic duck curve and probabilistic ramp curve are proposed. • The probabilistic duck curve is modeled considering dependencies among PV and loads. • Probabilistic duck curve is used for flexible resource planning. • Empirical analysis is conducted on actual high PV penetration power system in China. The high penetration of photovoltaic (PV) is reshaping the electricity net-load curve and has a significant impact on power system operation and planning. The concept of duck curve is widely used to describe the timing imbalance between peak demand and PV generation. The traditional duck curve is deterministic and only shows a single extreme or typical scenario during a day. Thus, it cannot capture both the probability of that scenario and the uncertainty of PV generation and loads. These weaknesses limit the application of the duck curve on power system planning under high PV penetration. To address this issue, the novel concepts of probabilistic duck curve (PDC) and probabilistic ramp curve (PRC) are proposed to accurately model the uncertainty and variability of electricity net load and ramp under high PV penetration. An efficient method is presented for modeling PDC and PRC using kernel density estimation, copula function, and dependent discrete convolution. Several indices are designed to quantify the characteristics of the PDC and PRC. For the application, we demonstrate how the PDC and PRC will benefit flexible resource planning. Finally, an empirical study on the Qinghai provincial power system of China validates the effectiveness of the presented method. The results of PDC and PRC intuitively illustrate that the ramp demand and the valley of net load face considerable uncertainty under high PV penetration. The results of flexible resource planning indicate that retrofitting coal-fired units has remarkable performance on enhancing the power system flexibility in Qinghai. In average, reducing the minimal output of coal-fired units by 1 MW will increase PV accommodation by over 4 MWh each day. [ABSTRACT FROM AUTHOR]

Highlights • A modified multi-period Nash-Cournot model is established. • The large-scale of renewables and flexible resources are considered in the model. • The renewables curtailment situations are considered in the model. • An empirical example is provided, based on the real data of BTT region, China. • Suggestions for future electricity and environment policies are presented. Abstract Facing stricter energy policies, the power mix in China is experiencing significant changes. First, the proportion of renewables, which have intermittent and stochastic generation, is assumed to be rapidly increasing. Thus, there is an increasing requirement to install more flexible generation capacity in the power system. According to the Chinese government’s energy planning, in the near future, the proportion of gas-fired units will be greatly promoted to add flexibility to the power system. The impacts incurred by developing a high proportion of both renewables and gas-fired generation on the power market should be formulated and analyzed. In this study, a modified Nash-Cournot equilibrium model is proposed, considering renewable curtailment situations during valley times. Then, a large-scale and multi-period unit commitment model that considers the combined heat and power characteristics is presented to simulate market behaviors. On this basis, an empirical analysis of the Beijing-Tianjin-Tangshan power system equilibrium is illustrated as numerical examples, presenting the influences of the changing power mix on electricity prices, renewable energy integration, carbon emissions and air pollutant emissions. Finally, some suggestions on future electrical and environmental policies are presented. [ABSTRACT FROM AUTHOR]

Some areas in China are facing pressing air pollution problems. Measures from the power sector can be taken to cope with air pollution issues, including reducing emission levels of thermal units and integrating wind and solar power. Social welfare, emission, and renewable integration are three major concerns in modern power system operations. This paper describes three stochastic scheduling models aiming at maximizing social welfare (SW), minimizing emission (EM) and maximizing renewable production (RE). A multi-objective scheduling model (MT) is also proposed that properly balances the above objectives. Wind power uncertainty and dispatchable loads are considered in the model. The outcomes of the three models are compared through an illustrative example and a 57-node case study. Results show that model EM results in 36% of the social welfare of model SW, 27% of its emissions, and 43% of its wind spillage, while model RE results in 55% of the social welfare of model SW, 56% of its emissions and 28% of its wind spillage. Additionally, we analyze how the optimal generation scheduling is affected by the weights in model MT. This work provides insight to policy makers on how to balance social welfare, emissions and renewable production. [ABSTRACT FROM AUTHOR]

Accommodating variable wind power poses a critical challenge for electric power systems that are heavily dependent on combined-heat-and-power (CHP) plants, as is the case for north China. Pumped hydro storage (PHS) and electric boilers (EBs) are two of the strongest technological options under discussion in China to address this challenge, but rigorous quantitative analyses of their effectiveness rooted in actual system data are lacking. An improved unit-commitment based power system chronological simulation is applied to evaluate potential benefits from PHS and EBs in West Inner Mongolia (WIM), where CHP capacity is projected to increase to 33.8 GW by 2020. A business-as-usual (BAU) reference model assumes deployment of 20 GW of wind capacity. Compared to BAU, expanding wind capacity to 40 GW would allow for a reduction in CO 2 emissions of 33.9 million tons, but at a relatively high cost of US$25.3/ton, reflecting primarily high associated curtailment of wind electricity (20.4%). A number of scenarios adding PHS and/or EBs combined with higher levels of wind capacity are evaluated. The best case indicates that a combination of PHS (3.6 GW) and EBs (6.2 GW) together with 40 GW of wind capacity would reduce CO 2 emissions by 43.5 million tons compared to BAU and at a lower cost of US$16.0/ton. Achieving this outcome will require a price-incentive policy designed to ensure the profitability of both PHS and EB facilities. [ABSTRACT FROM AUTHOR]

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]

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 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]

• 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]