A comprehensive review for wind,solar,and electrical load forecasting methods

Wind power,solar power,and electrical load forecasting are essential works to ensure the safe and stable operation of the electric power system.With the increasing permeability of new energy and the rising demand response load,the uncertainty on the production and load sides are both increased,bringing new challenges to the forecasting work and putting forward higher requirements to the forecasting accuracy.Most review/survey papers focus on one specific forecasting object(wind,solar,or load),a few involve the above two or three objects,but the forecasting objects are surveyed separately.Some papers predict at least two kinds of objects simultaneously to cope with the increasing uncertainty at both production and load sides.However,there is no corresponding review at present.Hence,our study provides a comprehensive review of wind,solar,and electrical load forecasting methods.Furthermore,the survey of Numerical Weather Prediction wind speed/irradiance correction methods is also included in this manuscript.Challenges and future research directions are discussed at last.

Power system planning with high renewable energy penetration considering demand response

Electric system planning with high variable renewable energy(VRE)penetration levels has attracted great attention world-wide.Electricity production of VRE highly depends on the weather conditions and thus involves large variability,uncertainty,and low-capacity credit.This gives rise to significant challenges for power system planning.Currently,many solutions are proposed to address the issue of operational flexibility inadequacy,including flexibility retrofit of thermal units,inter-regional transmission,electricity energy storage,and demand response(DR).Evidently,the performance and the cost of various solutions are different.It is relevant to explore the optimal portfolio to satisfy the flexibility requirement for a renewable dominated system and the role of each flexibility source.In this study,the value of diverse DR flexibilities was examined and a stochastic investment planning model considering DR is proposed.Two types of DRs,namely interrupted DR and transferred DR,were modeled.Chronological load and renewable generation curves with 8760 hours within a whole year were reduced to 4 weekly scenarios to accelerate the optimization.Clustered unit commitment constraints for accommodating variability of renewables were incorporated.Case studies based on IEEE RTS-96 system are reported to demonstrate the effectiveness of the proposed method and the DR potential to avoid energy storage investment.

Flexibility improvement evaluation of hydrogen storage based on electricity-hydrogen coupled energy model

To achieve carbon neutrality by 2060,decarbonization in the energy sector is crucial.Hydrogen is expected to be vital for achieving the aim of carbon neutrality for two reasons:use of power-to-hydrogen(P2H)can avoid carbon emissions from hydrogen production,which is traditionally performed using fossil fuels;Hydrogen from P2H can be stored for long durations in large scales and then delivered as industrial raw material or fed back to the power system depending on the demand.In this study,we focus on the analysis and evaluation of hydrogen value in terms of improvement in the flexibility of the energy system,particularly that derived from hydrogen storage.An electricity-hydrogen coupled energy model is proposed to realize the hourly-level operation simulation and capacity planning optimization aiming at the lowest cost of energy.Based on this model and considering Northwest China as the region of study,the potential of improvement in the flexibility of hydrogen storage is determined through optimization calculations in a series of study cases with various hydrogen demand levels.The results of the quantitative calculations prove that effective hydrogen storage can improve the system flexibility by promoting the energy demand balance over a long term,contributing toward reducing the investment cost of both generators and battery storage and thus the total energy cost.This advantage can be further improved when the hydrogen demand rises.However,a cost reduction by 20%is required for hydrogen-related technologies to initiate hydrogen storage as long-term energy storage for power systems.This study provides a suggestion and reference for the advancement and planning of hydrogen storage development in regions with rich sources of renewable energy.

Adjustable and distributionally robust chance-constrained economic dispatch considering wind power uncertainty

This paper proposes an adjustable and distributionally robust chance-constrained(ADRCC) optimal power flow(OPF) model for economic dispatch considering wind power forecasting uncertainty. The proposed ADRCC-OPF model is distributionally robust because the uncertainties of the wind power forecasting are represented only by their first-and second-order moments instead of a specific distribution assumption. The proposed model is adjustable because it is formulated as a second-order cone programming(SOCP) model with an adjustable coefficient.This coefficient can control the robustness of the chance constraints, which may be set up for the Gaussian distribution, symmetrically distributional robustness, or distributionally robust cases considering wind forecasting uncertainty. The conservativeness of the ADRCC-OPF model is analyzed and compared with the actual distribution data of wind forecasting error. The system operators can choose an appropriate adjustable coefficient to tradeoff between the economics and system security.

Constructing a V2G-enabled Regional Energy Internet for Cost-efficient Carbon Trading

The increasing penetration of plug-in electric ve- hicles (PEVs) has highlighted the importance of coordinating ubiquitous distributed energy resources (DERs) via the internet of things (IoT).With the help of vehicle-to-grid (V2G) technology, PEVs can be aggregated to behave as a storage system, yielding both economic and environmental benefits. In this paper, we propose an optimal bidding framework for a V2G-enabled re- gional energy internet (REI) to participate in day-ahead markets considering carbon trading. The REI operator aims to maximize the net profits from day-ahead markets while anticipating real- time adjustments. A detailed battery model is developed to depict the charging and discharging capability of V2G-enabled PEVs. A two-stage stochastic optimization model is formulated to schedule the operation of PEV fleets against various sources of uncertainties, e.g., the arrival and departure time of PEVs, solar power and real-time prices. Case studies undertaken based on realistic datasets demonstrate that the coordination of the V2G- enabled PEVs and other DERs can facilitate the accommodation of renewable energy, thus improving the REI’s revenues in energy and carbon markets.

Towards carbon neutrality:A study on China's long-term low-carbon transition pathways and strategies

As the world's biggest carbon dioxide(CO_(2))emitter and the largest developing country,China faces daunting challenges to peak its emissions before 2030 and achieve carbon neutrality within 40 years.This study fully considered the carbon-neutrality goal and the temperature rise constraints required by the Paris Agreement,by developing six long-term development scenarios,and conducting a quantitative evaluation on the carbon emissions pathways,energy transformation,technology,policy and investment demand for each scenario.This study combined both bottom-up and top-down methodologies,including simulations and analyses of energy consumption of end-use and power sectors(bottom-up),as well as scenario analysis,investment demand and technology evaluation at the macro level(top-down).This study demonstrates that achieving carbon neutrality before 2060 translates to significant efforts and overwhelming challenges for China.To comply with the target,a high rate of an average annual reduction of CO_(2) emissions by 9.3%from 2030 to 2050 is a necessity,which requires a huge investment demand.For example,in the 1.5℃ scenario,an investment in energy infrastructure alone equivalent to 2.6%of that year's GDP will be necessary.The technological pathway towards carbon neutrality will rely highly on both conventional emission reduction technologies and breakthrough technologies.China needs to balance a long-term development strategy of lower greenhouse gas emissions that meets both the Paris Agreement and the long-term goals for domestic economic and social development,with a phased implementation for both its five-year and long-term plans.

Coordinated Operation of Concentrating Solar Power Plant and Wind Farm for Frequency Regulation

As a dispatchable renewable energy technology, the fast ramping capability of concentrating solar power (CSP) can be exploited to provide regulation services. However, frequent adjustments in real-time power output of CSP, which stems out of strategies offered by ill-designed market, may affect the durability and the profitability of the CSP plant, especially when it provides fast regulation services in a real-time operation. We propose the coordinated operation of a CSP plant and wind farm by exploiting their complementarity in accuracy and durability for providing frequency regulation. The coordinated operation can respond to regulation signals effectively and achieve a better performance than conventional thermal generators. We further propose an optimal bidding strategy for both energy and frequency regulations for the coordinated operation of CSP plant and wind farm in day-ahead market (DAM). The validity of the coordinated operation model and the proposed bidding strategy is verified by a case study including a base case and sensitivity analyses on several impacting factors in electricity markets.

Locational Pricing of Uncertainty Based on Robust Optimization

With the increasing penetration of renewables,power systems have to operate with greater flexibility to address the uncertainties of renewable output.This paper develops an uncertainty locational marginal price(ULMP)mechanism to price these uncertainties.They are denoted as box deviation intervals as suggested by the market participants.The ULMP model solves a robust optimal power flow(OPF)problem to clear market bids,aiming to minimize the system cost as a prerequisite that the reserve margin can address all the relevant uncertainties.The ULMP can be obtained as a by-product of the optimization problem from the Lagrange multipliers.Under the ULMP mechanism,renewables and consumers with uncertainty will make extra payments,and the thermals and financial transmission right(FTR)holders will be compensated.It is further shown that the proposed mechanism has preferable properties,such as social efficiency,budget balance and individual rationality.Numerical tests are conducted on the modified IEEE 5-bus and 118-bus systems to demonstrate the merits and applicability of the proposed mechanism.