Search

The energy system and its energy performance of R-CELLS, a residential zero energy building from team Tianjin U+ in the Solar Decathlon China 2022, is introduced in this paper. When designing and constructing the R-CELLS energy system, two sets of challenges are encountered. Firstly, from an architectural perspective, the limited construction timeframe (only 21 days) and constrained space for housing the equipment, also ease of maintenance should be taken into account. Secondly, from an operational perspective, a constrained period for tests before the competition, a large number of devices dependent on electricity, heat and hot water supply should be considered. In consideration of the ease of assembly and maintenance of the energy system components and the aesthetic requirements of R-CELLS, an improved "photovoltaics, energy storage, direct current, flexibility" based energy system is utilized in R-CELLS. The R-CELLS energy system integrates various renewable energy sources (building integrated photovoltaics, photovoltaic-thermal, and building integrated wind turbines). Furthermore, it incorporates energy storage systems such as battery energy storage system, hot water tank, and phase change material thermal storage. In order to satisfy user requirements in grid-connected, off-grid and outage scenarios, including electricity, heat and domestic hot water, a tri-period energy management strategy based on optimized scheduling and rule-based control is proposed to achieve weekly, daily, and hourly net zero goals. Furthermore, the energy performance of R-CELLS is simulated for a whole year, including typical days in winter and summer, as well as the competition period. During the Solar Decathlon China 2022 competition, R-CELLS was subjected to testing by the committee. The results demonstrated that R-CELLS can be operated off-grid and with zero energy consumption, while meeting the test requirements for indoor environment, heating and cooling, and home life. • An improved PEDF based energy system of residential ZEB is utilized in R-CELLS, the champion competition entry from team Tianjin U+ in the Solar Decathlon China 2022. • A three-period EMS based on optimized scheduling and rule-based control is proposed. • Simulation data demonstrate that R-CELLS meets the ZEB standards. • The actual operation during the competition validates the reliable performance of the energy system, the effectiveness of the EMS, and the fulfillment of various energy needs of the residents. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources (RES) significantly increases, their inherent uncertainty has brought about a paradigm shift in distribution system planning and operation. The existing planning methodologies usually consider the uncertainty during the investment and day-ahead stages while neglecting the interactive power fluctuations during the intraday stage, resulting in planning schemes with a risk of inadequate regional stability. In light of this, this paper proposes a novel adaptive robust distribution system planning approach that integrates local uncertainty management (LUM) to provide a comprehensive and refined consideration of uncertainty. First, we introduce the concept of LUM that can effectively address the deviations between predicted and actual power of RES and load. Second, we propose the adaptive robust planning model for the distribution system, in which the investment and day-ahead operation are considered in the first stage, while LUM is considered in the second stage to maintain the local stability of interactive power. The proposed model can actively manage uncertainties of RES and load demand, reaching the optimal balance between the investment cost, operational cost, and LUM cost. Finally, by employing the column-and-constraint generation (C&CG), the adaptive robust planning model is decoupled into a master min problem and a slaver max-min problem, after which the max-min problem is converted into a single mixed-integer linear programming (MILP) problem through the strong duality theory and big-M method. The effectiveness of the proposed model is demonstrated using a real-world distribution system located in Fujian Province, China. The simulation results indicate that the proposed model achieves a quantitative decrease of approximately 10% in total cost compared to deterministic planning. Sensitivity analysis is also conducted to reveal the trade-off between economic cost and robustness level. • Local uncertainty management (LUM) is incorporated into distribution system planning. • A three-stage framework is developed for the planning problem incorporating LUM. • An adaptive robust distribution system planning model incorporating LUM is proposed. [ABSTRACT FROM AUTHOR]

As the integration of renewable energy sources (RES) progresses and energy technologies advance, integrated energy systems (IES) have emerged as a practical solution for leveraging RES and balancing local demand–supply dynamics. This paper investigates the planning and optimization of operations for an electricity-hydrogen integrated energy system (EH-IES), considering degradation and multi-timescale operations of storage devices to minimize the life cycle costs. To mitigate computational challenges, the planning model adopts a Representative Time Period (RTP) framework, and a novel intertemporal operation model is developed to coordinate the operations of battery and hydrogen storage. Furthermore, we propose a two-stage approach to derive representative weeks and hours from meteorological and load data using fewer data points, ensuring chronological coherence and preserving long-term patterns to validate storage device constraints and improve planning accuracy. Case studies in southeastern China's IES verify the effectiveness of our planning approach, which lowers equivalent annual costs by 4.5% and reduces battery capacity needs by 12.8%, thereby highlighting its significant economic benefits. Additionally, numerical analyses reveal that our two-stage method surpasses conventional RTP selection techniques, delivering at least 10% less average error with an equal number of operating points. • A novel planning and intertemporal model of EH-IES based on representative time periods is established. • The multi-timescale operations of the battery and hydrogen storage system is considered. • An efficient and accurate representative time-period selection method is developed. [ABSTRACT FROM AUTHOR]

Hydropower, an excellent power source for peak shaving, can respond quickly to load changes, which helps to overcome the anti-peak characteristics of wind and solar power. However, complementary operation with wind and solar power changes the operating boundaries of hydropower stations, making the original operating rules no longer applicable. Considering daily peak-shaving demands, this paper proposes a methodology to develop operating rules for a hydro–wind–solar hybrid system (HWSHS). First, a multi-objective and multi-timescale operation model is developed to ensure energy generation and peak-shaving performance simultaneously. Second, the feasible and reasonable region of available energy-based operating rules is analyzed, and three rule forms are employed to explore the optimal operating rules. Last, NSGA-II is utilized to optimize the rule parameters. A decoupling method is proposed to handle the shape constraints of operating rules, and the multi-timescale simulation method for a HWSHS is developed as the fitness function. In the simulation method, a load reconstruction-based load shedding (LRLS) method is introduced to determine the hourly output of each hydropower station. A forebay elevation check method is performed to obtain the hourly forebay elevation and correct the hourly output. The Beipan HWSHS in Guizhou Province, China, is selected as a case study, and the optimal operating rules are determined. The results also show that (1) the standard operating rules with hedging and packing increase peak-shaving performance by an average of 4.31% and 2.03% compared to the standard operating rules and linear operating rules, respectively; (2) the peak-shaving performance can be significantly increased (6.70%) with a slight decrease (0.37%) in the energy generation through changing the seasonal distribution of water and energy of the cascade hydropower stations; (3) the LRLS method, which makes the interday energy distribution more uniform, increases the peak-shaving performance by an average of 9.17% compared to the traditional load shedding method; (4) the energy generation and peak-shaving performance of the HWSHS are affected by the transmission capacity, and it is necessary to increase the transmission capacity by 25% to 35% of the installed capacity of wind and solar power stations. • A methodology is proposed to develop operating rules considering peak-shaving demands. • A multi-objective and multi-timescale operation model is developed. • The feasible and reasonable region of available energy-based operating rules is analyzed. • A multi-timescale simulation method is designed to simulate the operation of a HWSHS. • The methodology significantly improves the peak-shaving performance. [ABSTRACT FROM AUTHOR]

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines. The model moves renewable energy from renewable energy power plants to end users. [Display omitted] • MESS aims to solve uneven spatio-temporal distribution of renewable energy and load. • A framework for joint operation of power system and transportation system is proposed. • The model is validated based on real data from Northeast and North China. [ABSTRACT FROM AUTHOR]

Large-scale renewable integration presents an effective way to decarbonize power grids, but carries increased risk of supply shortfalls owing to its volatility and uncertainty. Storage is a promising option to improve the generation adequacy of renewable. Thus, capacity credit assessment of renewable and storage is crucial in ensuring adequate generation capacity to meet loads. However, efficiently and accurately assessing capacity credit of these resources is challenging due to temporal dependencies in the operational time series (net loads and conventional generation units) and the need for extensive operation simulations. This paper develops a comprehensive multi-time-scale assessment framework integrating analytical and simulation methods to calculate the capacity credit of renewable and storage, thus capturing temporal features of these time series. Then, an interval-based strategy is proposed to simulate system operations, incorporating demand response in key scenarios. Furthermore, partitioning around medoids clustering and parallel computing techniques are employed to greatly accelerate the numerous operations for capacity credit assessment. The proposed method is validated using the RTS-79 system and a provincial real-world power grid in China. The results indicate that the developed framework can achieve efficient and refined capacity credit assessment and thus evaluate the impact of storage on the capacity credit. • Propose a novel method to quantify temporal features of operational time series for capacity credit (CC) assessment. • Develop a multi-time-scale CC assessment framework with an interval-based strategy to capture temporal features. • Utilize partitioning around medoids clustering and parallel computing to greatly enhance calculation efficiency. [ABSTRACT FROM AUTHOR]

In the context of China's dual‑carbon goals and electricity market reforms, the high penetration of renewable energy sources (RES) in a single microgrid (MG) imposes challenges to its flexible operation. This paper addresses the cooperation within a virtual power plant (VPP) aggregated by multiple MGs. The VPP, managed by the VPP operator, acts as an intermediate entity to facilitate MGs' economic and low-carbon operation. To ensure the interests of various stakeholders in the VPP, this work proposes a low carbon oriented tri-stage collaborative energy management framework. In the 1st stage, the day-ahead optimal scheduling of individual MGs and the bidding strategy of the VPP operator are modeled, where an internal pricing method is incorporated. The 2nd stage addresses the real-time redispatch of the VPP operator using a receding horizon-based model predictive control (MPC) solution. This aims to satisfy the real-time operational requirements of MGs and reduce adjustment costs. Furthermore, in the 3rd stage, a penalty allocation mechanism is established for MGs based on their forecasting errors, to protect the VPP operator's interests. The effectiveness of the proposed solution is evaluated through comparative simulations in the context of the Guangdong electricity market. • The virtual power plant (VPP) aggregated by multiple microgrids (MG) is studied. • The quantification of carbon emission is involved in the whole scheduling process. • An peer-to-peer (P2P) electricity trading pricing method is improved and adopted. • A penalty allocation mechanism is proposed to guarantee interests of each entity. • The Guangdong electricity market is considered as the market mechanism. [ABSTRACT FROM AUTHOR]

Multiple geographically dispersed data center buildings have been increasingly positioned in renewable-rich areas due to their high energy consumption. This paper proposes an optimal multi-energy portfolio for zero carbon data center buildings in the presence of proactive demand response programs. In the data center buildings, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via multi-energy conversion and storage devices, which are modeled as 100% renewable energy hubs for zero carbon multi-energy supplies. In order to exploit the hub-internal operational dispatchability and flexibility, a comprehensive energy consumption model of data center buildings is proposed to facilitate the demand response in terms of available serves, while the variable speed pumped storage (VSPS) is modeled to capture its dynamic operational characteristics. The energy‑carbon production, conversion, storage, and consumption are formulated as an energy‑carbon matrix, which are solved via a two-stage method to obtain the optimal portfolio. Case studies on southwest China data center buildings are implemented to demonstrate the effectiveness and superiority of the proposed methodology on cost-effective accommodation of RESs. Simulations results show that the system portfolio cost can be reduced by at most 17.8% with a higher operational flexibility. • A cost-effective data center building is proposed to accommodate zero carbon targets. • Hydro-solar-wind 100% renewables are formulated for multi-energy supplies. • VSPS and data center energy consumption are modeled to exploit inherent flexibility. • The energy-carbon production, conversion, storage, and consumption are optimized. [ABSTRACT FROM AUTHOR]

• The analysis framework of a digital business model of PV fishery is constructed. • Multiple value propositions in the digital business model of PV fishery to create value for stakeholders. • The digital business model of PV fishery improves the operational efficiency of PV power generation. • The digital PV fishery increases the diversification of revenue sources of PV agricultural company. A business model that combines solar photovoltaic and fishery plays a role to expand distributed renewable energy and improve rural economy development. However, the business model of solar photovoltaic fishery is encountering growing difficulties with revenues decreasing due to changes of policy such as withdrawal of photovoltaic subsidies and land control. The spreading of digitization in photovoltaic industry and rural areas provides new opportunities for innovation of traditional solar photovoltaic fishery. This paper focuses on multiple value propositions to discuss a digital business model based of solar photovoltaic fishery on a case with a local Chinese photovoltaic agricultural company. The study results show that the digital business model of solar photovoltaic fishery improves the operational efficiency of solar photovoltaic power generation, economic benefits of aquaculture, and the diversification of revenue sources of solar photovoltaic agricultural company and leasing company. The transformation of business model from traditional solar photovoltaic power generation and fishery business to digital service platforms can not only accelerate expansion of distributed renewable energy and be potential deployed globally but also assist photovoltaic agricultural company to effectively respond to policy change. Finally, this paper proposes that supportive policy may focus on advocating system coupling to expedite the development of distributed renewable energies based on digital technology, and digital photovoltaic combined with rural energy market and financial, which will advance development of renewable energy electrification and promote rural economic growth. [ABSTRACT FROM AUTHOR]

Highlights • Novel formulation of unit commitment model for large-scale power system simulation. • Validated with standard test system and applied to real regional systems in China. • Increase the computational speed by more than 20,000 times with ∼1% error. • Can be applied to planning issues, e.g. optimal sizing of wind, solar and hydro. Abstract Deploying high penetration of variable renewables represents a critical pathway for decarbonizing the power sector. Hydro power (including pumped-hydro), batteries, and fast responding thermal units are essential in providing system flexibility at elevated renewable penetration. How to quantify the merit of flexibility from these sources in accommodating variable renewables, and to evaluate the operational costs considering system flexibility constraints have been central challenges for future power system planning. This paper presents an improved linear formulation of the unit commitment model adopting unit grouping techniques to expedite evaluation of the curtailment of renewables and operational costs for large-scale power systems. All decision variables in this formulation are continuous, and all chronological constraints are formulated subsequently. Tested based on actual data from a regional power system in China, the computational speed of the model is more than 20,000 times faster than the rigorous unit commitment model, with less than 1% difference in results. Hourly simulation for an entire year takes less than 3 min. The results demonstrate strong potential to apply the proposed model to long term planning related issues, such as flexibility assessment, wind curtailment analysis, and operational cost evaluation, which could set a methodological foundation for evaluating the optimal combination of wind, solar and hydro investments. [ABSTRACT FROM AUTHOR]

Highlights • Renewable quotas incorporating equity and duty are allocated at regional level. • The entropy method coupled with Management by Objectives principles is adopted. • Quotas scheme is dominated by differences in resource endowment and infrastructure. • Nexus for renewable-rich and developed areas is vital to fulfill renewable quotas. • Renewable revolution needs a twin-track process of nearby and external consumption. Abstract Feed-in tariff and renewable portfolio standards are two major policies for renewable power generation and China is the first country to adopt both policies at the national level. The black box for regional allocation of renewable portfolio standards, however, may lead to the instability and inefficiency of this policy. This paper introduces the principles of Management by Objective to build a clear policy framework for renewable portfolio standards in China and employs the entropy method to equally and reasonably assign regional responsibility for renewable energy development. A comparison is made between our allocation and the goal-directed policy to determine whether historical responsibility and infrastructure construction should be further considered in crafting renewable portfolio standards. The trends for regional renewable quotas are influenced by the twin-track approach of nearby utilization and external transmission. Finally, based on our analysis, the policy implications behind the new incentive are presented to urge the establishments of supporting facilities. [ABSTRACT FROM AUTHOR]

The objective of this study is to trace and quantify the sources of rebound effect for different energy sources in China. This paper decomposes the economy-wide rebound effect into 135 production sector-level rebound effects and five final demand components. The sector-level rebound is further decomposed into output effect and substitution effect. A two-stage decomposition method and a static computable general equilibrium model are developed to achieve this. Five types of energy-specific efficiency improvements are introduced, respectively, in all the production sectors. Results show that improving efficiency of using coal leads to the smallest macro-level rebound. For rebound decomposition, four findings are generalized. First, final consumption shows larger impact on the rebound of secondary energy sources than on primary energy sources. Second, production sectors that are big user of the efficiency-exposed energy source are expected to be key rebound contributor. Third, energy-producing sectors make negative rebound contribution. Results also show that, substitution effect is the predominant mechanism that triggers sector-level rebound. Policy implications are also discussed. [ABSTRACT FROM AUTHOR]

In an effort to address climate change, in 2013 China launched the world’s largest government-driven carbon emission reduction programme, the National Low Carbon Industrial Parks Pilot Programme (LCIPPP). This paper analyses this newly developed pilot program. To deepen our understanding of the causes and the impact of industrial park CO 2 emissions, we use the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and data from 20 pilot industrial parks involved in the LCIPPP for the period 2012–2016. This study quantitatively evaluates the effect of CO 2 emissions on output, energy structure, energy intensity, industrial structure, R&D intensity, and population change in different regions and nationally through an elasticity coefficient method. The results confirm that an increase in output and energy intensity is a dominant contributor to the growth of CO 2 emissions whereas an increase of the share of tertiary industry and R&D intensity has significant effects on reducing CO 2 emissions. The elasticity of energy intensity and renewable energy consumption on CO 2 emissions in the eastern region of China is the highest, indicating that using renewable energy to reduce CO 2 emissions for the industrial parks is more effective in the eastern region as compared to the central and western regions of the country. The elasticity of population is significantly negative in both the central and western areas while it is positive in eastern part of China, thereby illustrating that promoting labour intensive industries will be an effective way to reduce CO 2 emissions for the industrial parks in China’s central and western regions. Our study reveals that differentiated low carbon development pathways should be adopted. Concrete policy implications for reducing CO 2 emissions are also provided. [ABSTRACT FROM AUTHOR]

• Proposing a new robust co-planning of AC/DC hybrid transmission network and energy storage. • Embedding BFM within the proposed co-planning model. • Using data-adaptive robust optimization to cope with uncertainties. • Validating the effectiveness of the proposed method in practical power system. This paper proposes a robust co-planning model of hybrid AC/DC transmission network and energy storage with the penetration of renewable energy to promote the accommodation of renewable energy and to avoid investment redundancy. The energy storage configured in the power grid can improve the power flow distribution and alleviate transmission congestion, postponing the investment of new devices. A deterministic co-planning model is firstly developed considering voltage fluctuation, reactive power flow and the flexibility of voltage source converter based high voltage direct current (VSC-HVDC). To address the solving complexity caused by the non-convexity of the model, second-order cone programming (SOCP) is applied to transform the proposed model into a convex problem. To cope with the uncertainty of renewable energy, the robust co-planning formulation is established, where the data-adaptive uncertainty set with the extreme scenario method is introduced to describe renewable generation uncertainty. The column-and-constraint generation (C&CG) algorithm is adopted to decompose the robust co-planning problem into a master problem and several slave problems, which reduces the calculation scale and accelerates the solving process. Two case studies on a modified Garver's 6-bus system and a practical Jiangxi province power system in China are carried out to verify the effectiveness and superiority of the proposed robust co-planning model. [ABSTRACT FROM AUTHOR]

• Define and quantify hydropower flexibility in power regulation and frequency response. • Flexibilities are coordinated in day-ahead scheduling of hydro-wind hybrid power system. • Benefits and costs of hydropower providing flexibility. • Suggested schemes weighing benefits and costs under different capacity ratios. Uncertainty and instantaneous volatility of wind power make it crucial to schedule the hydropower scientifically to supply flexibility at multiple timescales in renewable energy hybrid power generation systems (RHPS). However, current day-ahead scheduling strategies do not consider the flexibility of time scales below the minimum scheduling time resolution, resulting in insufficient flexibility in intraday operation, power curtailment and load loss. Therefore, in this paper, day-ahead scheduling model coordinating power regulation flexibility (PRF) at 15 min timescale and frequency response flexibility (FRF) at seconds timescale is proposed for hydro-wind hybrid power generation system (HWHPS). Meanwhile, the ability, benefits, and costs of hydropower on supplying flexibility in HWHPS are studied in detail. First, the demand and supply for PRF and FRF are defined and quantified at different confidence levels considering the regulation characteristics of hydropower units. Then, a heuristic piecewise method is utilized to linearize the nonlinear FRF expression, and a day-ahead scheduling MILP model of the HWHPS is constructed. The MILP model is applied to a HWHPS composed of a hydropower station in southwest China and a virtual wind farm simulated based on the data representative for the hydropower station region. We found that the available hydropower flexibility has potential in reducing the maximum frequency deviation, wind power curtailment and load loss. Meanwhile, hydropower provides flexibility at the cost of hydro energy loss due to increased water consumption, especially for supplying FRF during periods of high wind penetration. Finally, since benefits and costs are affected by different confidence levels, a comprehensive index is proposed to evaluate different capacity ratios and scenarios to suggest confidence levels for day-ahead scheduling. Decision makers can also refer to the flexibility supply capacity of hydropower station to select the connected wind power capacity and system load to ensure comprehensive benefits. The methods, models and conclusions in this study can provide a valuable reference for the planning and dispatching of HWHPS, which contributes to the reliability and stability. [ABSTRACT FROM AUTHOR]

• The data envelopment analysis is introduced to renewable energy development. • The method has two stages: comprehensive evaluation and structure optimization. • Indexes from different aspects are quantified via intuitionistic fuzzy numbers. • The comprehensive performances are determined based on the relative efficiencies. • The proportions of renewable energy are optimized based on the whole efficiency. Different kinds of renewable energy resources have developed rapidly. For renewable energy planning, it is meaningful to assess the comprehensive performances of different schemes and then to determine the optimal design of the energy structure. This paper presents a two-stage method for the comprehensive evaluation and structure optimization of renewable energy plans. First, in the evaluation stage, multiple indexes from different aspects are taken into consideration, of which each qualitative index will be converted quantitatively, using the intuitionistic fuzzy number to describe the fuzziness and ambiguity in a qualitative index. Then, the superefficiency data envelopment analysis model is used to determine the comprehensive performances of different plans based on the concept of relative efficiency. Next, in the optimization stage, an optimal model combining multiple renewable energy resources is established based on the relative efficiency results of the evaluation process. This model aims at the maximum efficiency as a whole and can be used to optimize the proportions of different renewable energy resources. Finally, a real case of renewable energy development from a province in China is given to demonstrate the feasibility and effectiveness of the proposed method in this paper. The results show that it can overcome the shortcomings of the traditional basic model, obtain more objective evaluation results and provide beneficial references for the strategy making of renewable energy development. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for spatiotemporal coordination of multienergy systems is proposed. • Virtual energy storage gain is calculated based on spectral analysis. • The hydropower regulation ability affects the virtual energy storage gain. • The optimal coordination range between multiple energy sources is obtained. Utilizing the spatial heterogeneity and climate periodicity of various available renewable energy sources can enhance the multienergy complementarity, which will further reduce the energy storage demand and contributes to the "virtual energy storage gain." In this paper, we propose a spatiotemporal coordination method based on spectral analysis for a wind-PV-hydropower system that targets the maximum virtual energy storage gain. The complementary effect of hydropower on wind and PV power can be seen as changes in the regulation ability resulting from the hydropower construction development as well as a decreased variance in the total system production output. This method is used to determine the optimal coordination distance of multiple energy sources that are matched over different periods representing typical climate variation. A case study in the Yalong River basin in China obtains the best range of hydropower bundling surrounding wind power and PV power stations under different hydropower construction planning scenarios, and it reveals that increased regulation capacity of hydropower will reduce the optimal coordination distance but can achieve more energy balance if both actual storage and virtual storage is accounted for. This approach can realize energy delivery with the optimal coordination distance to meet intensive and efficient development needs, which can provide guidance and support for the planning and construction of wind-PV-hydropower storage systems. [ABSTRACT FROM AUTHOR]

• Develop an hourly-resolution, multi-region model with total system cost optimized. • Energy storage technology can meet 85.8%-by-2050 renewable energy penetration. • UHVDC transmission technology is the most economical way for RES integration. • Optimal combination of UHVDC grid and ES can achieve 74.2% renewables penetration. • The cost-optimal operation strategy varies among six regions in planning period. High renewable energy penetration is an effective way to decarbonize the power system. However, many studies focus on the economics of integrating renewables at the generation-side rather than system-wide. This paper aims to explore the cost-optimal operation strategies of a renewable-dominant power system. Considering both cost reduction potential of energy storage technology and regional investment cost variance of ultra-high voltage direct current grids, an hourly-resolution, multi-region model with total system costs minimized is proposed. Moreover, energy storage-based, ultra-high voltage direct current-based, and mixed scenarios are taken into consideration to investigate the economics of high renewables penetration. The results show that energy storage technology can achieve maximum penetration of renewable energy at 85.8% while the total system cost is 11.8% higher than that of ultra-high voltage direct current transmission technology. Although ultra-high voltage direct current grid is the most economical option to integrate renewables, the penetration is 13 percentage points lower than that of energy storage technology. The combination of energy storage technology and ultra-high voltage direct current grid can achieve 74.2% renewable energy penetration, saving 9.4% of total system costs compared to that of energy storage technology only. The cost-optimal option for East and South China is to promote both energy storage and ultra-high voltage direct current technologies. Energy storage technology is preferred among North, Northwest and Northeast China, while ultra-high voltage direct current grid is the optimal option for Center China. [ABSTRACT FROM AUTHOR]

The Chinese government identifies the renewable energy sector as a core strategic industry. Since 2009, China is the country with the highest annual investment into renewable energy, predominantly wind and solar photovoltaic projects. Due to rapid cost decline, industrial transformation, and policy support, the relative share of solar project investment is growing at a disproportionate rate. However, there is no systematic analysis of how efficiently these investments are allocated; or the underlying risk and return characteristics. Based on structured interviews with 69 market participants, this paper identifies severe cash-flow uncertainty, unreliable supply chains and a weak regulatory environment as most prominent risk factors that currently inhibit sustainable and demand-driven market development. Expanding on the interview results, this study discusses the origins of risk in China and how the institutional response to investment risk currently fails to address the core problems. [ABSTRACT FROM AUTHOR]

Renewable energy has a critical role in limiting the greenhouse gas (GHG) emissions. This paper assesses the implication of aligning renewable energy deployment target with national emission reduction target for mitigation cost. The assessment methodology uses Asia-Pacific Integrated Assessment/computable general equilibrium (AIM/CGE) model to determine the mitigation cost in terms of GDP and welfare loss under alternative renewable targets in different climate-constrained scenarios. A range of country-specific emission constraints is taken to address the uncertainties related to global emission pathway and emission entitlement scheme. Comparative results show that China needs to increase its share of non-fossil fuel significantly in the primary energy mix to achieve the stringent emission reduction target compared to India. The mitigation cost in terms of economic and welfare loss can be reduced by increasing the penetration of the renewable energy to achieve the same emission reduction target. The modeling results show that coordinated national climate and renewable energy policies help to achieve the GHG emission reduction target in an efficient and cost-effective manner. [ABSTRACT FROM AUTHOR]

Over the recent decades, China’s building energy consumption has been growing rapidly. Also the fuel mix has been changing quickly with more natural gas & electricity and less coal being used. Meanwhile, the technologies used in building sector are improving in efficiency. In this paper, the impact of technical progress and the use of renewable energy in building sector are analyzed. The energy saving and emission reduction potential of building sector are also measured. China TIMES model is used to model the future energy consumption and carbon emissions in building sector. The modelling results indicate that building energy consumption is expected to grow to around 41.6EJ in the reference scenario in 2050. The energy saving potential in 2050 can be up to 4EJ due to the improvement of both building insulation (envelop) and energy use technologies. Renewable energy used in buildings can be a great contributor to the carbon mitigation in buildings. China’s building sector can reach a relatively low-carbon future with more low- and non-carbon fuels consumed. [ABSTRACT FROM AUTHOR]

• A chance-constrained co-optimization model for generation and reserve scheduling of cascade hydro-VRE hybrid systems. • Nonlinear dependence between generation efficiency and hydropower reserves is modeled. • Economic allocation of VRE reserve requirements among cascade hydropower stations. • A solution approach for chance constraints based on quantile regression theory. The rapid development of variable renewable energy (VRE), such as wind and solar energy, has stimulated the complementary operation of VRE with flexible cascade hydropower stations in China. Due to the uncertainties of VRE power generation and complex constraints of cascade hydropower stations, formulating reliable day-ahead generation and reserve scheduling plans is a real challenge in the actual operation stage of the cascade hydropower–VRE hybrid systems (CHVHS). In this paper, we propose a tractable chance-constrained co-optimization model for day-ahead generation and reserve scheduling of a CHVHES. First, compared with existing models, the relationship between the hydropower reserve capacities and water-electricity conversion efficiency is finely modeled. Accordingly, the economic allocation of total VRE reserve requirements among cascade hydropower stations is for the first time considered in our proposed model. This can save more water resources for cascade hydropower stations when compensating for VRE, further improving hydro–VRE complementary profits. Second, we propose a solution approach for chance constraints by incorporating the nonparametric probabilistic forecasting of VRE power based on quantile regression into the chance-constrained model, ensuring that the stochastic dependence between VRE power output and its point forecast can be effectively captured. Importantly, this solution approach does not require prior knowledge or any probability distribution assumptions of VRE power and does not introduce any additional computational burden. With the help of three-dimensional interpolation technology for nonlinear constraints, the proposed scheduling model is finally cast as a mixed-integer linear programming model that is computationally tractable. Numerical tests implemented on a real CHVHES located in Southwest China verify the effectiveness and advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

• A wind power prediction method based on ensemble learning and hyper-parameters optimization is proposed. • A wind power sequence segmentation method based on the Swing Window is proposed. • The relationships mapping model between error sequence and NWP sequences are built. • A daily rolling error correction strategy is proposed to correct wind power prediction results. With a high proportion of renewable energy injected into the power grid, the accurate wind power prediction of wind farms is key to improving power quality and ensuring the stable operation of the power grid. In this paper, an ensemble learning prediction model considering the rolling error correction strategy used for short-term wind power prediction is proposed. Firstly, the ensemble learning prediction model integrating multiple Gradient Boosting Trees (GBDTs) based on Bayesian optimization is established, and the characteristics of prediction errors are analyzed. To further improve the prediction accuracy, a daily rolling error correction strategy based on the Swing Window segmentation method, Spearman correlation coefficient and Quantile Regression is established to obtain the optimal compensation amount. Finally, the corrected short-term wind power prediction results are obtained. The two-year dataset collected from a regional wind farm in China is used as the benchmark test. Different prediction models and error correction strategies are compared, and the effectiveness of the proposed method is comprehensively evaluated. The results from different seasons show that the proposed prediction method has good stability, and the proposed error correction strategy has good generalization ability. [ABSTRACT FROM AUTHOR]

• Integrating machine learning and vine-copula to catch the complex spatial correlations among the renewable resources. • Utilization of stochastic model predictive control to optimize the long-term hydro-wind-solar operation. • Potential assessment of hydropower to offset the effects of seasonal uncertainties and mismatch between renewable resources and requirements. It is popular to combine the hydropower plant with the wind and solar power plants to supply electricity, and the joint is termed as the hydro-wind-solar renewable energy supply system (RESS). The long-term optimal operation of the RESS is a challenging task, due to the nature of different spatial and temporal variabilities associated with renewable energy resources, and the significant operation uncertainties due to the changing natural environment. To address the task, the stochastic model predictive control (MPC), based on probabilistic forecasting and rolling stochastic optimization, is designed and implemented for the long-term operations of the RESSs in Yunnan province, China. The paper tests out the system efficiencies for different penetration levels of wind and solar power, and finds out that (1) the Long Short-Term Memory performs best among candidate point prediction algorithms; (2) the stochastic MPC considering the correlations among renewable resources helps the RESS operate with a higher efficiency; (3) the large-scale hydropower plant has great potential to offset the effects of seasonal uncertainties and demand-generation mismatch, but cannot completely avoid the electricity shortage enabled by unexpected scenarios of renewable resources. [ABSTRACT FROM AUTHOR]

Abstract: Currently one of the most promising power solutions for remote rural areas and isolated islands which are far away from utility grid is the stand-alone solar photovoltaic (PV) renewable energy system or hybrid solar–wind power system. This paper presents the investigation results of a real remote solar PV project on an island in Hong Kong. The stand-alone PV system, rated at 19.8kWp, is established on a remote island for power generation to local residents. Power on this island was originally supplied by diesel generators and this new solar energy system has significantly reduced fuel costs and alleviated environmental pollution. The on-site entire system’s operating data was collected for one whole year in 2011, and the energy performance of the PV array, inverters, battery bank and the entire PV system were analyzed in terms of daily energy balance, normalized performance parameters and overall system energy utilization ratio. The results from this long term system monitoring and its evaluation enables a detailed inspection of system operating performance with respect to the technical issues, which provides a useful reference for future applications of solar photovoltaics in remote islands. [Copyright &y& Elsevier]

• Inequality of renewable technology innovation is evaluated by Gini coefficients. • Contributors to renewable innovation inequality are decomposed by Shapley method. • Variables of regional attributes, policies and official characteristics are proposed. • Knowledge stock and R&D expenditure are the greatest contributors. • Energy-related experience and tenure of officials may be potential contributors. Regional inequality of technology innovation associated with renewables may lead to insufficient innovation growth over the long-term, which impedes the deployment of renewable energy and slows down low-carbon transition. This paper aims to explore the major contributors to regional inequality of technology innovation in renewable energy by taking China as a representative example. Three groups of variables including regional attributes, supporting policies, and local official characteristics are proposed as potential explanatory factors. A set of panel data covering both the photovoltaic and wind energy sectors in 31 Chinese provinces from 2008 to 2017 are collected for the empirical study. Gini coefficient is used to quantify the inequalities of renewable technology innovation, while Shapley decomposition method is applied to evaluate the contribution of each significant factors. The results show that the overall Gini coefficients of photovoltaic and wind technology innovation in China are 0.65 and 0.62, respectively, indicating medium-level regional technology innovation inequality in both sectors. Knowledge stock (46.44%), R&D expenditure (36.56%), green fixed industrial investment (13.53%), electricity consumption (1.33%), and the energy-related experience of local officials (0.05%) are significant contributors to regional inequality of photovoltaic innovation. Knowledge stock (45.20%), R&D expenditure (37.90%), electricity consumption (8.63%), feed-in tariffs (1.02%), and the tenure of local officials (0.18%) are significant contributors to regional inequality of wind innovation. Therefore, eliminating regional inequality of technology innovation in renewables should start with balancing regional R&D expenditure and knowledge stock. [ABSTRACT FROM AUTHOR]

Abstract: Renewable energy is one of the possible solutions when addressing climate change. Today, large-scale renewable energy integration needs to include the experience to balance the discrepancy between electricity demand and supply. The electrification of transportation may have the potential to deal with this imbalance and to reduce its high dependence on oil production. For this reason, it is interesting to analyse the extent to which transport electrification can further the renewable energy integration. This paper quantifies this issue in Inner Mongolia, where the share of wind power in the electricity supply was 6.5% in 2009 and which has the plan to develop large-scale wind power. The results show that electric vehicles (EVs) have the ability to balance the electricity demand and supply and to further the wind power integration. In the best case, the energy system with EV can increase wind power integration by 8%. The application of EVs benefits from saving both energy system cost and fuel cost. However, the negative consequences of decreasing energy system efficiency and increasing the CO2 emission should be noted when applying the hydrogen fuel cell vehicle (HFCV). The results also indicate that developing renewable energy is crucial for transportation electrification. [Copyright &y& Elsevier]

Abstract: China has set ambitious goals to increase the use of renewable energy. Developing renewables in rural areas is also one of the most important energy strategies. This paper examines rural social acceptance of renewable energy deployment taking Shandong as a case study via a field questionnaire survey. Theory of planned behavior is adopted to establish an analytical framework, and a logit model is used to examine possible determinants of local social acceptance. The results show that rural residents are generally supportive renewable energy development given its positive impacts on environment. A stated willingness to pay more for renewable electricity is taken as a variable representing an individual’s behavioral intention. The probability of occurrence of positive intention is found to increase with household income, individual knowledge level and belief about costs of renewable energy use but decrease with individual age. Residents with higher level of income are more likely to be willing to pay more for green electricity, so are the younger people. Enhancive knowledge and understanding about renewable energy (for instance, the cost) would be conducive to win public acceptance of renewable energy deployment. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to identify the relations between transportation energy consumption and its impacted factors. We first analyze the current status of transportation energy consumption in China. Then, the logarithmic mean Divisia index (LMDI) technique is used to find the nature of the factors those influence the changes in transportation energy consumption. We find that: (1) In 2006, the transportation energy consumption increased by 7.63 times against that in 1980. (2) Up to 2006, the oil consumed by transportation accounted for 49.6% of that in the whole country, which almost equaled to the net oil import. (3) In the light of the increasing energy consumption intensity, the energy-utilization effectiveness of transportation sector has been declining gradually. (4) The transportation activity effect is the most important contributor to increase energy consumption in the transportation sector and the energy intensity effect plays the dominant role in decreasing energy consumption. [Copyright &y& Elsevier]

Abstract: Along with high-speed economic development and increasing energy consumption, the Chinese Government faces a growing pressure to maintain the balance between energy supply and demand. In 2009, China has become both the largest energy consumer and CO2 emitting country in the world. In this case, the inappropriate energy consumption structure should be changed. As an alternative, a suitable infrastructure for the implementation of renewable energy may serve as a long-term sustainable solution. The perspective of a 100% renewable energy system has been analyzed and discussed in some countries previously. In this process, assessment of domestic renewable energy sources is the first step. Then appropriate methodologies are needed to perform energy system analyses involving the integration of more sustainable strategies. Denmark may serve as an example of how sustainable strategies can be implemented. The Danish system has demonstrated the possibility of converting into a 100% renewable energy system. This paper discusses the perspective of renewable energy in China firstly, and then analyses whether it is suitable to adopt similar methodologies applied in other countries as China approaches a renewable energy system. The conclusion is that China’s domestic renewable energy sources are abundant and show the possibility to cover future energy demand; the methodologies used to analyse a 100% renewable energy system are applicable in China. Therefore, proposing an analysis of a 100% renewable energy system in China is not unreasonable. [ABSTRACT FROM AUTHOR]

• An optimal configuration method of concentrating solar power in power systems. • A data-driven scenario generation method to describe the uncertainty in power systems. • The benefits of concentrating solar power are comprehensively considered. • Sensitivity factors affecting the configuration results are analyzed in the case study. The proportion of renewable energy sources and the flexibility demands of the power grid have increased simultaneously, which has caused difficulties in renewable energy consumption and the secure operation of power systems. Concentrating solar power (CSP) can provide additional flexibility for power systems and change the uncontrollable characteristics of variable renewable energy generation. Due to the high construction cost, it is necessary to fully evaluate the configurational scheme of CSP. In this paper, we propose an optimal configuration method for CSP in multienergy power systems to fully utilize the CSP benefits. We first improve the variational autoencoder (VAE) to describe the uncertainty in power systems and generate scenarios for the configuration model. The model consists of two stages: planning and operation. The planning model determines the configuration capacity of each component of the CSP station. The operation model considers the day-ahead and real-time periods to analyze the operational cost of the power systems with CSP. By means of linearized methods, the configuration model is treated as a mixed integer linear programming (MILP) formulation to obtain a rapid solution. A case study is conducted based on the power source data of a province in northwestern China. The simulation results show that flexibility values makes CSP more competitive in the configuration process, and the configuration results are significantly affected by the coordination with other power sources. We use sensitivity analysis with different situations to explore the development prospects of CSP. [ABSTRACT FROM AUTHOR]

• A modified dynamic recursive CGE model is constructed. • Renewable energy growth is considerable only if ETS revenue is used. • Broader renewable energy subsidies are better than ones with narrow scope. • A small part of ETS revenue should be used to subsidize residents for social stability. Emission Trading Scheme (ETS) and renewable energy generation are emission reduction methods in most countries in the world. However, few studies have focused on the impact of ETS on renewable energy. The question is, can carbon trading promote renewable energy generation? This paper first analyzes different distribution strategies of ETS revenue by applying dynamic recursive computable general equilibrium model with multi-sectors. Practical scenarios and better options of distribution of ETS revenue by a comprehensive evaluation based on entropy weight method are proposed. The results show that ETS with no subsidy to renewable will reduce the demand for energy, increase the cost of renewable energy sources and decrease the generation. ETS will be the spring of renewable energy generation when most of the revenue is used for all kinds of renewable energy sources, instead of some of them. The growth of renewable energy generation is also substantial. It is necessary that a small portion of ETS revenue should be used to subsidize residents to reduce the gap between the rich and the poor. If this income is used for government investment and consumption, it will also help to mitigate economic losses, which is caused by the direction of investment by the Chinese government. [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]

• China's Nationally Determined Contributions do not comply with the Paris Agreement. • Overall coal usage needs to decrease by 60% (2 °C) or even 100% (1.5 °C) by 2050. • 100% Renewable energies are cost-efficient to decarbonize the electricity sector. • Sector coupling increases the electricity demand by 100% (2 °C) to 400% (1.5 °C). • Incentives for local actors are needed for a sustainable low-carbon transformation. Growing prosperity among its population and an inherent increasing demand for energy complicate China's target of combating climate change, while maintaining its economic growth. This paper, therefore, describes three potential decarbonization pathways to analyze different effects for the electricity, transport, heating, and industrial sectors until 2050. Using an enhanced version of the multi-sectoral, open-source Global Energy System Model, enables us to assess the impact of different CO 2 budgets on the upcoming energy system transformation. A detailed provincial resolution allows for the implementation of regional characteristics and disparities within China. Conclusively, we complement the model-based analysis with a quantitative assessment of current barriers for the needed transformation. Results indicate that overall energy system CO 2 emissions and in particular coal usage have to be reduced drastically to meet (inter-) national climate targets. Specifically, coal consumption has to decrease by around 60% in 2050 compared to 2015. The current Nationally Determined Contributions proposed by the Chinese government of peaking emissions in 2030 are, therefore, not sufficient to comply with a global CO 2 budget in line with the Paris Agreement. Renewable energies, in particular photovoltaics and onshore wind, profit from decreasing costs and can provide a more sustainable and cheaper energy source. Furthermore, increased stakeholder interactions and incentives are needed to mitigate the resistance of local actors against a low-carbon transformation. [ABSTRACT FROM AUTHOR]