Transactional Strategies Research Articles

Energy-efficient scheduling for a hybrid flow shop problem while considering multi-renewable energy

At present, the concept of green and sustainable development has been recognised by everyone. For industrial applications, the concept is both a matter of energy consumption and a matter of production methods. Based on the hybrid flow shop problem, a multi-energy supply system is constructed, and external renewable energy microgrid is introduced to further improve the low-carbon level. From the perspectives of energy supply and internal load, the output of wind power and photovoltaic, energy storage cost and energy consumption cost in the flow shop on a certain day are analysed under the demand–response electricity price. After considering the natural endowments and demand differences of different microgrids, construct a multi-energy complementarity model, determine the coordination transaction strategy among microgrids and obtain the solution of the subproblem and the equilibrium solution of the whole problem. Finally, an example is given to evaluate the effectiveness of the proposed strategy under time-of-use prices. The results show that the proposed strategy can effectively improve the utilisation rate of renewable energy in the flow shop while reducing the overall operating cost.

Continuous auction mechanism model for safety electric energy market transaction

With the rapid development of microgrid in the electric power industry, the microgrid electric energy transaction has begun to be marketized, and the research on the microgrid electric energy trusted transaction has important theoretical research value and social value. The existing blockchain-based microgrid electric energy trusted transaction models mostly focus on energy management and scheduling control between microgrids when conducting electric energy transactions, and do not fully consider the bidding problems in the market-based transaction of microgrid electric energy, resulting in trading strategies are difficult to adapt to new market changes. In response to this problem, this paper proposes a reliable transaction approach for microgrid electric energy based on a continuous two-way auction mechanism. The proposed strategy accounts for the volatility of electricity prices in the microgrid trading market and employs the continuous two-way auction mechanism to evaluate the microgrid electricity trading tactics. In the microgrid electric energy transaction, the self-adaptive learning theory is applied to adjust the quotations of both parties, so that both parties can make reasonable quotations according to the market environment. By simulating experimental data, the findings indicate that the continuous two-way auction mechanism transaction strategy enables both parties to modify their quotations based on market transaction information, thereby displaying a high degree of flexibility in microgrid electricity’s market-oriented trading.

Contract Theory-Based Collection, Updating, and Packaging Combination Strategies in Data Trading

In the data transaction process, where a single supplier collects and sells data to heterogeneous data buyers, its transaction strategy relies on the data’s value. In this paper, we measure the value of data in terms of volume and currency and analyze the whole process from data collection and update management to sales. The data supplier determines the maximum amount of data to be collected based on the willingness to pay and the cost of collection and decides the optimal update frequency based on the update cost paid to improve the data utility. Because data sets can be replicated and split nearly costlessly, data suppliers maximize revenue by combining data sets of different sizes. In the information asymmetry scenario, contract theory is applied to obtain the optimal data collection quantity, update frequency, and packaging combination scheme to maximize the profit of data suppliers under data buyer incentive compatibility and individual rationality and compare them with the information symmetry scenario. The results show that data suppliers will only provide personalized data sets to some data buyers, and the needs of some buyers will not be met. Information asymmetry causes data suppliers to reduce the number of data collections and the actual purchases by data buyers, and data suppliers’ profits tend to decrease. The surprising conclusion is that data suppliers should set up more frequent update strategies in the information asymmetry case than in the information symmetry case to sustain the decrease in data utility caused by insufficient quantity. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72071042 and 72201113]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2023.0018 .

Cross-regional green certificate transaction strategies based on a double-layer game model

With the implementation of the renewable energy quota system and the development of the green certificate market, the diversification of cross-region trading subjects has brought about the problem of complicating the competition and cooperative relationship between multiple subjects. This problem makes it difficult to balance the conflict of interests of multiple participating subjects in green certificate transactions, and reduces the enthusiasm for trading. To this end, this paper proposes a multi-participant decision model of cross-regional green certificate trading behavior that takes into account the uncertainty of renewable energy generation. In the model, in order to explore the potential full set of strategies of the participating subjects, the cooperative game theory is utilized to analyze the complex interaction between multiple participating subjects in the cross-regional transaction, and the cooperative game model based on green certificates is established. As the participating subjects' information about other participants and their knowledge of the game environment are limited, the decision-making behavior is uncertain. In order to more reasonably portray the subject's game behavior in the actual decision-making scenario and to solve the defect that the traditional evolutionary game model can not reflect the influence of uncertain factors, we use the stochastic evolutionary game theory to dynamically deduce the cooperative strategies of the participating subjects under the limited rationality, and at the same time, introduce the strategy influencing factors to explore the dependence relationship between strategies. In addition, in order to cope with the impact of power generation uncertainty on cross-regional trading and to solve the defects of distributionally robust fuzzy set construction which is subjective and only for the analysis of uncertainty in a single time section, it is proposed to use Bayesian multivariate nonlinear model and mixed-effects model to construct probability distribution fuzzy sets of wind and light power generation uncertainty on the basis of distributionally robust theory. Finally, based on the arithmetic example, the cooperative strategy of multi-participants in cross-regional transactions under finite rationality is deduced, and the impact of uncertainty factors on the cooperative strategy is analyzed.

Analysis on innovation management of power financial transaction strategy integrating BO-BERT-GRNN model

This paper addresses the innovation management problem of financial trading strategies for power system planning through the utilization of the BO-BERT-GRNN model. The BO-BERT-GRNN model, which combines Bayesian optimization, BERT model, and gated recurrent neural network, is divided into three parts to optimize hyperparameters, extract features from historical data, and model and predict power system planning. The objective is to achieve electricity asset allocation, market risk management, and revenue maximization. Experimental analysis demonstrates that the BO-BERT-GRNN model outperforms in power system planning price prediction, energy transaction risk management, and energy asset allocation, showcasing its potential for practical application. This paper addresses the innovation management problem of financial trading strategies for power system planning through the utilization of the BO-BERT-GRNN model. The BO-BERT-GRNN model, which combines Bayesian optimization, BERT model, and gated recurrent neural network, is divided into three parts to optimize hyperparameters, extract features from historical data, and model and predict power system planning. The objective is to achieve electricity asset allocation, market risk management, and revenue maximization. Experimental analysis demonstrates that the BO-BERT-GRNN model outperforms in power system planning price prediction, energy transaction risk management, and energy asset allocation, showcasing its potential for practical application.

Optimal Scheduling Framework of Integrated Energy System Based on Carbon Emission in Electricity Spot Market

Micro coal-fired units (MCFU) and combined heat and power plants (CHP) in integrated energy system (IES) will emit a large amount of carbon dioxide when providing loads to customers, which will lead to higher operating costs of IES. To solve this challenge, an optimal dispatch model of power-to-gas (P2G) and methane reactor (MR) considering the reward and punishment costs based on carbon emission trading mechanism is proposed, to reduce carbon emissions of IES and enhance the accommodation of renewable energy (RE). Subsequently, considering the uncertainty of RE, a combination optimization method for MCFU and CHP units is developed based on the Lagrange multiplier method. Finally, considering the mechanism of electricity spot market (ESM), a transaction strategy for IES participating in ESM is proposed to further enhance the accommodation of RE. The effectiveness of the proposed framework is demonstrated through simulations.

INNOVATIVE APPROACHES TO TEACHING SPEAKING: A CASE STUDY OF SHORT FILM PROJECTS

This study was conducted as an attempt to find out the effect of applying Transactional Strategies Instruction (TSI) on students' reading comprehension. This study used an experimental design. The population were students from SMA Negeri 1 Onan Ganjang. The sample for this study consisted of sixty first-graders at a senior high school. Two randomly generated groups—one for the experimental group and one for the control group—were used. The control group was taught without applying TSI, while the experimental group was taught with TSI. The instrument for collecting data was an objective multiple-choice test, which consisted of 40 items. The data were analyzed using a t-test. This study shows that teaching reading comprehension by using TSI has a more significant effect than teaching without it.

Research on Policy configuration Scheme based on improved ARIMA Model

In this paper, we use the principle of different models to remove the trend from the stock time series and choose the SARIMA [1, 1, 1][0,1,0] 12 models to fit the stock price. The results show a good prediction performance. Then a ternary transaction strategy is proposed, accurately identifying value changes regarding cash as financial products (expected rate of return = 0%, cost = 0%), and establishes a ternary asset flow model. Combining the expected rate of return and volatility, we model the value of existing financial products. Based on the trend accuracy to evaluate the accuracy of stock price prediction, the SARIMA model shows high accuracy in the verification set, and the maximum root mean square error (RMSE) is 11. 5494. The final five-year return is 51973 times higher than the bank savings and fixed investment return.

Can CSR strategy mediate conflict over extraction? Evidence from two mines in Peru

We argue that corporate social responsibility (CSR) strategies can shape political contexts to mediate or exacerbate the resource curse. Using a relational pragmatic approach—one that recognizes actors are dynamic and focuses on the interactions that shape how they see their interests—we develop expectations about two ideal type CSR strategies: transformational and transactional and their relational implications. We demonstrate the usefulness of this approach through the examination of two mines in Peru. Drawing on fieldwork conducted in 2017 and 2018 and secondary research, we show how one mine’s transformational strategy connected the company to common, or public, concerns in ways that rearticulated politics to dampen curse dynamics. The second mine’s transactional strategy narrowed its local engagement in ways that reduced its influence and played into curse dynamics. This research illustrates both the value of pragmatic approaches for integrating CSR into governance and the way in which CSR strategies can help mitigate the resource curse.

Managing real estate risk with environmental due diligence : Transaction strategies for environmentally impacted properties

Transacting on environmentally impacted properties provides a unique investment opportunity in the real estate market. Environmental risks need to be evaluated and quantified to take advantage of investment opportunities and manage liabilities. This paper provides an overview of the environmental due diligence process, outlines strategies for transacting environmentally impacted properties, and discusses the risks associated with various due diligence approaches and types of contaminated properties.

The Prospect Theory and The Stock Market

This paper is aimed to investigate the important principle of behavioral economics , prospect theory and applies prospect theory to stock market. Based on the former studies, there are three features at the core of prospect theory, and they are in turn the reference point, a principle of diminishing sensitivity and loss aversion. Prospect theory focus on how people make decisions in an uncertain situation and it combines psychology to research economic man's choice behavior rather than research rational people’s economic behavior like expected utility theory. Therefore, the prospect theory has more value and more practical to apply in the stock market and be studied to predict investors behavior. In this essay, case analysis method and theoretical analysis method will be used to explore the prospect theory and stock market. It found that prospect theory can conduct investors gain more profit because of stock reverse transaction strategy. And loss aversion, the most important factor of reversal effect, point out that when face the uncertainty, people won’t keep completely rational as the utility theory said. What’s more, it suggests the exist of reverse effect in Chinese stock market. The research contributed to present understanding of the prospect theory in China's stock market, and further explores that the stock reversal strategy is more conducive to investors' profits in China's stock market. A large part of the reasons for this phenomenon can be explained by prospect theory, while expected utility theory cannot.

Multidiscipline Design Optimization for Large-Scale Complex Nonlinear Dynamic System Based on Weak Coupling Interfaces

For high-tech manufacturing industries, developing large-scale complex nonlinear dynamic systems must be taken as one of the basic works, formulating problems to be solved, steering system design in a more preferable direction, and making correct strategic decisions. By using effective tools of big data mining, Dynamic Design Methodology was proposed to establish a technical platform for Multidiscipline Design Optimization such as High-Speed Rolling Stock, including three key technologies: analysis graph of full-vehicle stability properties and variation patterns, improved transaction strategy of flexible body to MBS interface, seamless collaboration with weldline fatigue damage assessments through correct Modal Stress Recovery. By applying the above methodology, a self-adaptive improved solution was formulated with optimal parameter configuration, which is one of the more favorable options for higher-speed bogies. While within a velocity (140–200) km/h at λe < 0.10, car body instability’s influence on ride comfort can be easily improved by using a semi-active vibration reduction technique between inter-vehicles through outer windshields. Comprehensive evaluations show that only under rational conditions of wheel-rail matching, i.e., 0.10 ≥ λeN > λemin and λemin = (0.03–0.06), can this low-cost solution achieve the three goals of low track conicity, optimal route planning, and investment benefit maximization. So, rail vehicle experts are necessary to collaborate and innovate intensively with passenger transportation and steel rail ones. Specifically, by adopting rail grinding treatment, occurrence probability is controlled at 85% and 5% for track conicity of (0.03–0.10) and (0.25–0.35). By optimizing routing planning, operating across dedicated lines of different speed grades can achieve self-cleaning of central hollow tread wear over time. According to the inherent rigid-flex coupling relationship, geometric nonlinearities of worn wheel-rail contact should be avoided as much as possible for HSR practices. Only under weak coupling interfaces in the floor frame can the structural integrity of an aluminum alloy car body be ensured.

Patient Complaints to the Saudi Medical Call Center: Representative Response Strategies

ABSTRACT This study aims to investigate the strategies and modifications employed by the Complaint Unit Representatives (CURs) in response to complaints recorded in the formal workplace of a medical institution in the Saudi Ministry of Health (MOH). A pragmatic discourse analytic approach was adopted to construct an analytical framework for authentic spoken complaint responses in the Saudi medical institution context. The data were randomly collected from 80 recorded phone conversations between patients and the CURs. It was transcribed verbatim, imported into MAXQDA for qualitative analysis of codes categorizations, and then into SPSS for statistical analysis. The findings showed that the staff employed a balance of transactional and interpersonal approaches in their response strategies, which varied in both quantity and quality depending on the phase or the major sequence of the moves made in the complaint call. Specifically, more transactional strategies were used during the main portion of a complaint and in the medial phase, while more interpersonal strategies were employed during the initial and the final phases of the call. The findings also showed that the CURs tended to downgrade and mitigate their responses to patients’ complaints, and they never used upgraders. The influence of the religious culture was also noticeable in their use of downgraders, including optimistic devices and religious expressions. These findings reveal practical implications that can aid the quality team of the Complaint Unit (CU) in assessing the effectiveness and efficiency of the CURs’ response strategies when handling complaints, and in providing more effective communication training as needed.

Strategic Peer-to-peer Energy Trading Framework Considering Distribution Network Constraints

With the development of smart home energy management technology, prosumers are endowed with increased initiative in peer-to-peer (P2P) transactions, bringing new potential for cost savings. In this study, a novel strategic P2P energy trading framework is proposed considering the impact of network constraints on personal transaction strategies. Prosumers can estimate the allowed power injection before engaging in the P2P energy trading, which is solved in a distributed manner based on the sharing form alternating direction method of multipliers (ADMM) algorithm. To quantify the network usage cost for each prosumer and promote local transactions among prosumers at the same bus, a modified continuous double auction (CDA) matching algorithm is proposed including a transaction fee. An adaptive aggressiveness-based bidding strategy is generated considering the risk of uncertainty in real-time energy delivery amount under the limitations of the distribution network. The proposed strategic P2P energy trading framework is tested with the IEEE 37-bus distribution network and it is effective in creating profits for prosumers and supporting distribution network operations.

Summary of Demand-side Response and Energy Transaction Strategy of Intelligent Building Clusters Driven by Data

Summary of Demand-side Response and Energy Transaction Strategy of Intelligent Building Clusters Driven by Data

Economical heat recovery dynamic control and business model for supermarket refrigeration system coupled with district heating system

Large amounts of waste heat during the cooling process of supermarket refrigeration systems (SRS) would be released. A heat recovery strategy potentially contributes to reducing the supermarket’s heating costs related to buying heat from a district heating system (DHS). This paper explores the techno-economic feasibility of heat recovery for a real SRS integrated with a heat recovery unit (HRU) in terms of designing a dynamic heat recovery control (HRC) and business models. A cost-effective HRC is firstly developed for HRU to optimally manipulate the amount of heat recovered, thereby minimizing the real-time heat recovery cost. Furthermore, the business models of heat recovery under a long-term operation are proposed based on two transactional strategies between the SRS and DHS. A field test of the dynamic heat recovery for a remote SRS in Copenhagen Nordhavn area is conducted which demonstrates the proposed HRC algorithm can have a benefit of 0.49€ from a 3-h heat recovery operation. Moreover, the one-year operation of the SRS is also simulated which proves the developed two business models of heat recovery can achieve significant savings of 93% and 41% in energy costs. • Data-driven cost-effective heat recovery control of a real SRS. • Two heat recovery business models for SRS having an energy transaction with DHS. • Field test of a real SRS based on remote controls to validate the proposed methods. • Economical real-time heat recovery of SRS operating with a lower recovery cost. • Sharp reduction of energy cost for SRS based on the proposed business model.

A Credibility Theory-Based Robust Optimization Model to Hedge Price Uncertainty of DSO with Multiple Transactions

This paper addresses the deregulated electricity market arising in a distribution system with an electricity transaction. Under such an environment, the distribution system operator (DSO) with a distributed generator faces the challenge of electricity price uncertainty in a spot market. In this context, a credibility theory-based robust optimization model with multiple transactions is established to hedge the uncertain spot price of the DSO. Firstly, on the basis of credibility theory, the spot price is taken as a fuzzy variable and a risk aversion-based fuzzy opportunity constraint is proposed. Then, to exploit the resiliency of multiple transactions on hedging against uncertain spot price, the spot market, option contract and bilateral contract integrating power flow constraints are studied, because it is imperative for DSO to consider the operational constraints of the local network in the electricity market. Finally, the clear equivalence class is adopted to transform the risk aversion constraint into a deterministic robust optimization one. Under the premise of considering the expected cost of the DSO, the optimal electricity transaction strategy that maximizes resistance to uncertain spot price is pursued. The rationality and effectiveness of the model are verified with a modified 15-node network. The results show that the introduction of option contracts and bilateral contracts reduces the electricity transaction cost of DSO by USD 28.5. In addition, under the same risk aversion factor, the cost of the proposed model is reduced by USD 195.18 compared with robust optimization, which avoids the over-conservatism of traditional robust optimization.

What implementation strategies are relational? Using Relational Theory to explore the ERIC implementation strategies.

The identification and use of implementation strategies in implementation research and practice have strengthened our understanding of the implementation process as well as the causal pathways between mechanisms, strategies, and implementation outcomes. Although these contributions have advanced the application of strategies, there is still a need to learn more about how strategies might integrate relational exchanges and interactions. The inclusion of critical perspectives has been limited in implementation science, and theories such as Relational Theory can expand our understanding of the relational nature of implementation and enhance rigor through alternative theoretical applications. This study applied Relational Theory through a qualitative directed content analysis of the 73 Expert Recommendations for Implementation Change (ERIC) implementation strategies and examine relational components in strategy descriptions. Three reviewers used the structured approach to review and categorize the implementation strategies based on the Relational and Transactional Strategy Continuum measure, which operationalizes types of interactions, exchanges and alliances. Relational alliance strategies are those in which there is mutual growth and accountability, frequent interaction, shared power, and potential vulnerability. Operational alliances include forms of working exchanges between parties with balanced transactional and relational features. Operational alliances can be somewhat interactive in nature, with minor exchanges and limited accountability. Transactional alliance strategies are mostly uni-directional, influenced by power differentials, and do not require mutual growth, commitment, or exchange; thus, the power of growth is inherently one-sided. Results from the review suggest more implementation strategies with relational alliance features (highly relational, n = 17, semi-relational, n = 19) compared to transactional (highly transactional, n = 9, semi-transactional, n = 10) and 18 strategies coded as operational alliances. The qualitative review revealed opportunities to further expand how relational exchanges are considered within the implementation strategies descriptions, as well as the role of actors and power dynamics within strategy exchanges. The Relational and Transactional Strategy Continuum measure can help practitioners and researchers consider the sequencing, pairing, and impact on outcomes of different types and combinations of strategies in implementation practice and research. Additionally, the measure can support reflection on strategies that promote positive alliances, frequent connections, bi-directional communication, and power sharing.

Grid-Oriented Coordination Strategy of Prosumers Using Game-theoretic Peer-to-Peer Trading Framework in Energy Community

In this paper, a grid-oriented peer-to-peer (P2P) energy transaction strategy based on game theory that considers participants with diversity in trading and the impact on the system of trading is studied. For this, the optimization problem of prosumers with ESS is defined according to the lifetime cost of the ESS. The proposed energy trading strategy is carried out based on three game theories. Among them, the evolutionary game between buyers, non-cooperative games between sellers, and Stackelberg games between sellers and buyers are considered. When all games are played sequentially and the convergence point is reached, the negotiation is determined and then ends and a contract is made based on trading price and amount. As a novel contribution in the case of the evolutionary game the community manager indirectly provides information on the system so that when evaluating the seller, the effect on the system is applied as a loss change. In addition, when deriving a utility value by payoff, it is set as an increase/decrease ratio compared to the initial value so that the game proceeds regardless of the size of the participants and the defined problem. To verify the effectiveness of the proposed P2P energy trading strategy, a simulation based on MATLAB is performed. It is confirmed that the effect on the grid is applied to the seller's selling price. When the strategy proposed in the P2P energy trading is applied, the increased purchase and selling amount due to the trading increased by 3.41 % and 4.51 %, respectively, he difference between increased puchase and selling amount decreases by about 36 % from 3.181 kW to 2.03 kW, and the loss due to the change in trading amount also decreased by 1.59 %. This means that the grid impact has been reflected in the P2P energy trading, resulting in efficient operation, and it is the reason for grid operators to accept P2P energy trading. In conclusion, the results of the proposed strategy prove that efficiency in energy use is increased by considering the impacts of the power system in P2P energy trading.

A novel trading strategy based on BiLSTM prediction model

In order to assist traders with scientific transaction strategy, this paper constructs a decision model including a prediction model and a trading model. Our predicting model is based on a BiLSTM (Bi-direction Long Short-Term Memory) neural network, and two linear layers trained by previous known data, which obtains a high accurate prediction of price. For the trading model, we construct it with several impact and quantitative artificial-selected-factors including market potential, deviation rate, risk score, etc. To use the model, we firstly uses previous known data to train the deep neural network and utilize it to make future predictions, the results of which is then imported into our trading model for decision making and better configure the portfolio. Generally speaking, our whole decision system achieves effective prediction of price, enables timely risk assessment, and makes scientific decisions by considering these factors together.