Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

Online risk‑based portfolio allocation on subsets of crypto assets applying a prototype‑based clustering algorithm

Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long.This is precisely the case in the cryptocur-rency market,where there are hundreds of crypto assets that have been traded for a few years.We propose enhancing the mean-variance(MV)model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period.In the pre-selection stage,we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality.The prototypes of the clustering partition are auto-matically examined and the one that best suits our risk-aversion preference is selected.We then run the MV portfolio optimization with the crypto assets of the selected cluster.The proposed approach is tested for a period of 17 months in the whole cryp-tocurrency market and two selections of the cryptocurrencies with the higher market capitalization(175 and 250 cryptos).We compare the results against three methods applied to the whole market:classic MV,risk parity,and hierarchical risk parity methods.We also compare our results with those from investing in the market index CCI30.The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators.This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

Dynamic assets allocation based on market microstructure model with variable-intensity jumps

In order to characterizc large fluctuations of the financial markets and optimize financial portfolio, a new dynamic asset control strategy was proposed in this work. Firstly, a random process item with variable jump intensity was introduced to the existing discrete microstructure model to denote large price fluctuations. The nonparametric method of LEE was used for detecting jumps. Further, the extended Kalman filter and the maximum likelihood method were applied to discrete microstructure modeling and the estimation of two market potential variables： market excess demand and liquidity. At last, based on the estimated variables, an assets allocation strategy using evolutionary algorithm was designed to control the weight of each asset dynamically. Case studies on IBM Stock show that jumps with variable intensity are detected successfully, and the assets allocation strategy may effectively keep the total assets growth or prevent assets loss at the stochastic financial market.

Review of Methods to Calculate Congestion Cost Allocation in Deregulated Electricity Market

With maturing deregulated environment for electricity market, cost of transmission congestion becomes a major issue for power system operation. Uniform Marginal Price and Locational Marginal Price (LMP) are the two practical pricing schemes on energy pricing and congestion cost allocation, which are based on different mechanisms. In this paper, these two pricing schemes are introduced in detail respectively. Also, the modified IEEE-14-bus system is used as a test system to calculate the allocated congestion cost by using these two pricing schemes.

Dynamic Promotional Resource Allocation for Segment Specific and Spectrum Effect of Promotion for a Product Line Incorporating Repeat Purchase Behavior

Promotion is an essential element in the marketing mix. It is used by businesses to inform, influence and persuade customers to adopt the products and services they offer. Without promotion, business would be stagnant and lack substantial growth because the brands would have low visibility in the market. Moreover, today’s vast and assorted markets comprise of customers with different needs and varied behavior. So it is rarely possible for companies to satisfy all customers by treating them alike. Thus there arises a need to divide the market into segments having customers with similar traits/characteristics. After identifying appropriate market segments, firms can design differentiated promotional campaigns for each segment. At the same time there can be a mass market promotional campaign that reaches different segments with a fixed spectrum. Also since promotional effort resources are limited, one must use them judiciously. In this paper, we formulate mathematical programming problem under repeat purchase scenario, which optimally allocates mass promotional effort resources and differentiated promotional effort resources across the segments dynamically in order to maximize the overall sales obtained from multiple products of a product line under budgetary and minimum sales aspiration level constraint on each product under consideration in each segment. The planning horizon is divided into multi periods, the adoption pattern of each product in each segment is observed in every subinterval and accordingly promotional effort allocations are determined for the next period till we reach the end of planning period. The optimization model has been further extended to incorporate minimum aspiration level constraints on total sales for each product under consideration from all the segments taken together. The non linear programming problem so formulated is solved using differential evolution approach. A numerical example has been discussed to illustrate applicability of the model.

UNIFORM-PRICE AUCTION FOR BANDWIDTH ALLOCATION IN THE INTERNET

It has been widely accepted that auctioning which is the pricing approach with minimal information requirement is a proper tool to manage scare network resources. Previous works focus on Vickrey auction which is incentive compatible in classic auction theory. In the beginning of this letter, the faults of the most representative auction-based mechanisms are discussed. And then a new method called Uniform-Price Auction (UPA), which has the simplest auction rule is proposed and its incentive compatibility in the network environment is also proved. Finally, the basic mode is extended to support applications which require minimum bandwidth guarantees for a given time period by introducing derivative market, and a market mechanism for network resource allocation which is predictable, riskless, and simple for end-users is completed.

Allocation of congestion relief cost based on flow change on the congested facilities

Congestion management in an electricity market is introduced in this paper and a new method of allocating congestion cost to transactions is proposed. The proposed method is a two-step process, in which the total congestion cost is firstly allocated to congested facilities and then to each transaction involved. The cost of relieving a congested facility allocated to each transaction is proportional to the power flow change on the congested facility caused by the transaction. The more the power flow change is on the congested facility caused by the transaction, the deeper the degree of involvement by the transaction. Therefore, cutting down the magnitudes of such transactions contributes to relieving congestion. Test results on a 5-bus system indicate that the proposed method can reflect reasonably the degree of involvement by each transaction in the congestion and provide correct price signals contributing to relieving congestion.

Adaptive computational resource allocation for sensor networks

To efficiently utilize the limited computational resource in real-time sensor networks, this paper focuses on the challenge of computational resource allocation in sensor networks and provides a solution with the method of economics. It designs a microeconomic system in which the applications distribute their computational resource consumption across sensor networks by virtue of mobile agent. Further, it proposes the market-based computational resource allocation policy named MCRA which satisfies the uniform consumption of computational energy in network and the optimal division of the single computational capacity for multiple tasks. The simulation in the scenario of target tracing demonstrates that MCRA realizes an efficient allocation of computational resources according to the priority of tasks, achieves the superior allocation performance and equilibrium performance compared to traditional allocation policies, and ultimately prolongs the system lifetime.

The Influence of Allowance Allocation Methods on CO2 Emission Reduction： Experiences From the Seven China Pilots

One of the key elements influencing the performance of a carbon trading system, are the methods of allocating the initial CO2 emissions. This paper tries to use a quantitative description method to analyze the influence of the different allocation methods on the level of CO2 emissions based on the seven pilot trading markets from 2009 to 2013 in China. The results show that different methods bring about various degrees of impacts, through direct and indirect constraint mechanism, influence the CO2 emission cut finally. Although due to the complexity of the direct and indirect constraint mechanism, attempting to compare the effects of different allocation methods is difficult by using the data of carbon emission cut from seven pilot markets in China, the paper shows that the allowance allocation methods, through the constraints imposed on enterprises, significantly reduce regional carbon emissions.

中国城市数字经济发展的资源配置效应

优化资源配置是建设全国统一大市场的基本目标,基于市场价格、竞争与风险机制的市场化视角,以中国城市数字经济发展实践为基础,构建“数字经济—市场化—资源配置”的理论分析框架,并运用全国地级市样本数据,在测算城市数字经济和资源配置效率指数的基础上实证分析。实证发现:中国城市数字经济发展显著改善了资源配置扭曲,这一结论在考虑指数测度误差、内生性问题以及外生政策冲击等因素后依然成立;市场化是数字经济提高资源配置效率的有效途径;数字经济对资源配置扭曲的优化效应存在边际递减的非线性特征,且在优化效应的结构差异上存在“资本—产品—劳动力”的递进逻辑;中国城市数字经济发展能够改善城市间产品和资本配置扭曲相互强化的内生交互循环,但也可能因城市间的“虹吸效应”导致劳动力配置在空间上产生“新扭曲”现象。

全国统一大市场如何推进企业绿色创新?——来自上市公司的经验证据

贯彻新发展理念是新时代我国高质量发展的必由之路,如何通过释放市场需求红利将全国统一大市场优势转变为企业绿色创新优势成为值得关注的重点问题。本文采用基于城市层面的市场潜力指标衡量全国统一大市场优势,利用上市企业绿色专利申请数量和被引次数分别反映企业绿色创新数量和质量,进而研究全国统一大市场优势对企业绿色创新的影响。结果显示,全国统一大市场优势显著促进企业绿色创新数量和质量的提高。进一步的作用机制分析表明,全国统一大市场通过减少资源错配、促进企业增加创新投入和减少企业销售成本等影响企业绿色创新。考虑不同企业所有制性质,不同行业、地区的企业绿色创新异质性,发现全国统一大市场对国企、重污染行业、中西部地区绿色创新效果更为显著。进一步考虑政府干预作用后的结果表明,地方政府的过度干预弱化了全国统一大市场优势的充分发挥。文章的研究结论对中国充分发挥全国统一大市场优势、鼓励企业绿色创新等具有重要的理论和现实意义。

数据要素市场化配置的法治推进——兼论《数据二十条》相关条款设计

数据要素市场化配置的程度与水平决定了充分释放数据要素动能的程度,对加快数据产业链、价值链重构与转型升级意义重大。数据要素市场化配置的前提是明确数据产权归属,基础是构建统一开放的数据要素市场,发展是健全高效的定价机制,底线是保障数据要素安全发展。我国虽已明确了数据这一新型生产要素的重要地位,但相关法律尚未明确数据的产权归属,全国统一的数据要素市场尚未建立,数据要素定价机制尚未明确,数据要素市场交易与监管的边界也尚未清晰,所有这些阻碍了数据要素市场化配置的充分实现。故需立足数据要素市场的特征与现实,以数据产权结构性分置制度促进数据动态权属流动,构建统一开放的数据要素交易市场机制,同时厘清数据监管边界,保障数据要素市场安全发展,实现数据要素高水平、高质量、高效率的市场化配置。

数据要素市场化配置对数实融合的影响研究——基于数据交易平台设立的准自然实验

数字经济与实体经济深度融合将推动全国统一大市场建设和双循环新发展格局的构建,成为经济增长的新动能。基于数据交易平台设立的准自然实验,采用耦合评价模型测度2010—2021年全国280个地级市的数字经济与实体经济融合水平,以多期双重差分法实证检验以数据交易平台设立为表征的数据要素市场化配置对数实融合的影响。研究发现:数据要素市场化配置显著促进了数字经济与实体经济的融合,该结论经一系列稳健性检验依旧成立;机制分析表明,数据要素市场化配置通过成本效应、要素配置效应、技术创新效应等路径影响数字经济与实体经济的融合;异质性分析表明,数据要素市场化配置对数实融合的提升效应在产权保护程度较高、行政等级较高及市场化程度较高的城市表现更为明显;拓展性分析发现,数据要素市场化配置对数字经济与实体经济融合的影响存在空间溢出效应。因而应加强数据交易平台建设,多维度探索数实融合路径,挖掘试点政策潜力,加强区域主体的交流协作,优化数据要素市场化配置,更好推动数字经济与实体经济的融合发展。

数据要素应用与企业创新效应——来自中国A股上市公司的经验证据

数据要素作为数字经济时代的核心生产要素,是驱动企业创新的新引擎。基于数据要素的多重异质属性,界定了企业层面数据要素应用的核心内涵,从数据资源、数据应用过程、数字技术和数字业务支撑系统四个维度筛选关键词,通过文本分析测度了数据要素应用程度指标,并以2007—2021年中国A股上市企业为样本,实证检验了数据要素应用对企业创新数量和创新质量的影响效应。研究发现:数据要素应用同时促进了企业创新数量增长和创新质量提升,该结论在内生性处理和稳健性检验后依然成立。机制分析表明,数据要素应用通过资源配置优化效应和市场偏好匹配效应促进企业创新量变和质变。基于环境动态性视角的进一步检验发现,数字基础设施环境优化会强化数据要素驱动企业创新量变,制度环境改善和技术更迭能同时激发数据要素引领企业创新的量变和质变。

人口老龄化视角下的家庭金融资产配置——基于CHFS的分析

为探究人口老龄化对于金融资产配置的影响,本文基于2019年中国家庭金融调查(CHFS),从深度与广度两个层面探究了家庭金融资产配置的生命周期特征。研究发现,风险资产比重在青壮年时期变化不大,而在55岁之后显著减少;股票资产占比、风险资产种数与年龄呈倒U型关系,并在45至55岁达到峰值;现金与银行存款的占比与年龄呈正相关关系。与此同时,本文分析了老龄化程度加深对家庭金融资产配置的影响,发现老年人口占比提升对家庭风险金融市场参与和风险资产配置产生了显著的抑制作用,而这一抑制作用受养老保险覆盖率与收入的调节,并存在地域异质性。机制分析发现,主观风险态度与金融素养是年龄结构影响家庭金融资产配置的重要途径。最后,针对我国人口老龄化背景下的家庭金融资产配置问题,本文结合实证所得结论提出相应的政策建议。

农地资源市场配置法律机制的完善

我国以土地承包经营权和土地经营权为基础的农地市场配置的法律机制,既存在市场机制的固有缺陷,也存在农村集体经济组织和集体土地所有权缺位的特定性局限。为实现市场对农地资源的有效配置,需充分发挥农村集体经济组织的民事治理作用和政府的市场管理与调控作用。应当完善农地市场民事法律机制,明确农村集体经济组织介入农户承包地经营权流转的法律地位和权利,扩大农村集体经济组织流转土地经营权的范围,完善农村集体经济组织的监督权利。应当完善对农地市场的管理调控法律机制,更好地发挥政府在农地用途管制、农业补贴政策和价格调控、产业政策引导、健全市场服务体系以及营造良好市场环境等方面的作用。

“十五五”时期充分发挥市场在资源配置中的决定性作用研究

面向“十五五”时期,充分发挥市场在资源配置中的决定性作用,应围绕社会经济活动、要素配置层次、社会主义经济制度三个维度,以及竞争性效率和帕累托效率两个效率视角,来理解其理论逻辑。要从“十五五”我国处于加快推进基本实现社会主义现代化的关键期,加快构建新发展格局着力推动高质量发展的攻坚期,推进国家治理体系和治理能力现代化的突破期,工业化、城镇化、信息化和农业现代化“四化同步”的加速期以及全面实施高水平制度型开放的窗口期几个层面,来把握充分发挥市场在资源配置中的决定性作用的重大意义。应通过重大改革举措和体制机制创新,充分发挥市场在资源配置中的决定性作用:以实现中国特色社会主义市场经济体制更加成熟更加定型为重点,加快优化市场经济基础制度;以破除传统要素自由流动堵点和建立新型要素市场规则为重点,全面推动要素市场化配置改革;以服务国家重大战略和破除发展的突出矛盾和明显短板为重点,积极健全重点领域的市场机制;以全面推进实现国家治理体系和治理能力现代化为重点,持续完善国家宏观经济治理体系;以对接高标准国际经贸规则和“边境后”规则适应性调整为重点,强化与制度型开放联动推进市场化改革。

数据要素市场化赋能银行数字化转型了吗?——来自中国城市层面的经验证据

深耕数据要素市场化沃土,以释放数据要素价值助力银行数字化转型,是做好数字金融大文章,充分发挥金融供血机能的关键举措。本文基于2010—2021年284个地级市面板数据,深入探讨数据要素市场化对银行数字化转型的影响。研究结果表明,数据要素市场化有助于推动银行数字化转型,同时这种影响在城市能级、金融韧性和商业信用环境方面表现出显著的异质性特征。作用机制分析发现,资源配置效率是数据要素市场化促进银行数字化转型的作用渠道,金融科技和银行业竞争在数据要素市场化赋能银行数字化转型过程中发挥正向调节效应。经济后果检验发现,数据要素市场化对银行数字化转型的促进效果可进一步作用于实体经济的发展。研究结果为银行数字化转型的实现路径提供了理论和实践参考。