Consumption Plans Research Articles

Optimal control strategy for building HVAC systems: Satisfying flexible demand response with different value-based selection

Growing renewable energy integration leads to significant fluctuations in electricity generation. To address this challenge, market-based mechanisms for dynamic carbon emission factors and electricity prices are emerging, particularly in regions like China pursuing carbon neutrality. Buildings, with HVAC systems as the largest consumers, require flexible Demand Response (DR) strategies to balance these fluctuations while maintaining occupant comfort. This paper proposes a hierarchical control framework for building HVAC systems. It optimizes operation for various objectives (minimizing energy consumption, reducing electricity costs, and lowering carbon emissions), while ensuring thermal comfort. The framework involves three modules. First is the time-based demand allocation, which uses genetic algorithms to optimize energy use based on future cooling demands, indoor temperature, and fluctuating electricity and carbon prices. It provides building-level temperature setpoints and electricity consumption plans. Second is the spatial indoor demand adjustment, which balances overall cooling load and temperature satisfaction across individual spaces, taking into account room-specific costs and difficulty confidents for temperature adjustments. This module assigns future temperature setpoints to each space. Third is HVAC system follow-up control, which Implements the optimized setpoints for real-time control of the HVAC system. The framework’s effectiveness is verified through application to a real commercial building project. Compared to a baseline scenario, the optimal electricity cost strategy achieved a 6.5% cost reduction, and the optimal carbon emission reduction strategy achieved an 8.2% reduction. Real-world control based on carbon emission optimization resulted in an actual year-on-year carbon emission reduction of 8.7%.

Recursive two-stage evaluation model for dynamic decision making under ambiguity

In this paper, the two-stage-evaluation (TSE) model for decision making under ambiguity (He 2021) is extended to intertemporal setting in an axiomatic approach. The first set of axioms employed are commonly adopted for dynamic non-expected utility models in the literature. Besides these regular axioms, I also assume dynamic consistency and conditions which deliver a static TSE for consumption plans only pay non-zero consequences for one period. It is shown that these axioms hold if and only if these exists a recursively defined evaluation utility model representing decision maker (DM)’s preferences over consumption plans conditional on arriving at any node in an event tree. Such a recursive form implies that one can apply dynamic programming technique (rolling back the decision tree) to solve a dynamic decision making problem under TSE model. It can be shown that the solution for the recursively defined dynamic TSE model exists uniquely. Due to the “small domain” setup, the agent is short-sighted in the sense that they only process subjective probabilities over events defined on one period uncertainty over next period states, which differs from “far-sighted” assumption in most extant models that assume subjective probabilities exist over events defined on multiple periods. It is shown that under some extra conditions, our DM applies Bayes’ rule “updating’ her subjective beliefs.

Deep reinforcement learning based dynamic pricing for demand response considering market and supply constraints

This paper presents a Reinforcement Learning (RL) approach to a price-based Demand Response (DR) program. The proposed framework manages a dynamic pricing scheme considering constraints from the supply and market side. Under these constraints, a DR Aggregator (DRA) is designed that takes advantage of a price generator function to establish a desirable power capacity through a coordination loop. Subsequently, a multi-agent system is suggested to exploit the flexibility potential of the residential sector to modify consumption patterns utilizing the relevant price policy. Specifically, electrical space heaters as flexible loads are employed to cope with the created policy by reducing energy costs while maintaining customers' comfort preferences. In addition, the developed mechanism is capable of dealing with deviations from the optimal consumption plan determined by residential agents at the beginning of the day. The DRA applies an RL method to handle such occurrences while maximizing its profits by adjusting the parameters of the price generator function at each iteration. A comparative study is also carried out for the proposed price-based DR and the RL-based DRA. The results demonstrate the efficiency of the suggested DR program to offer a power capacity that can maximize the profit of the aggregator and meet the needs of residential agents while preserving the constraints of the system.

Non-invasive load monitoring based on deep learning to identify unknown loads.

With the rapid development of smart grids, society has become increasingly urgent to solve the problems of low energy utilization efficiency and high energy consumption. In this context, load identification has become a key element in formulating scientific and effective energy consumption plans and reducing unnecessary energy waste. However, traditional load identification methods mainly focus on known electrical equipment, and accurate identification of unknown electrical equipment still faces significant challenges. A new encoding feature space based on Triplet neural networks is proposed in this paper to detect unknown electrical appliances using convex hull coincidence degree. Additionally, transfer learning is introduced for the rapid updating of the pre-classification model's self-incrementing class with the unknown load. In experiments, the effectiveness of our method is successfully tested on the PLAID dataset. The accuracy of unknown load identification reached 99.23%. Through this research, we expect to bring a new idea to the field of load identification to meet the urgent need for the identification of unknown electrical appliances in the development of smart grids.

Carbon-embedded energy coordination strategy in park-level integrated energy system considering time-varying carbon emission measurement

Energy efficiency and carbon mitigation are important issues in modern energy systems research. Considering the couplings between energy and carbon, this paper investigates a carbon-embedded energy coordination problem in a park-level integrated energy system (PIES). Firstly, based on energy hub, a multilateral interactive transaction framework is introduced, consisting of energy hub operator (EHO), building users with photovoltaics, and an electric vehicle (EV) charging agent. Secondly, a time-varying carbon emission measurement model for the outsourced electricity is designed, taking into account the dynamic composition of the supply-side generators. Based on the time-varying carbon factor and virtual carbon emission flow, an energy-carbon pricing strategy is proposed to balance the carbon responsibility among different participants. Then, a multi-agent interactive trading model in the PIES is constructed, where the energy consumption plans of building users and EVs are guided by the dynamic energy-carbon integrated prices. Through the iterative interaction among different agents, the optimal trading results are finally obtained. Simulation results illustrate the effectiveness of the proposed energy-carbon integrated pricing method in reducing carbon emissions and promoting energy sharing.

Optimal consumption for recursive preferences with local substitution — the case of certainty

We characterize optimal consumption policies in a recursive intertemporal utility framework with local substitution. We establish existence, uniqueness, and a version of the Kuhn–Tucker theorem. The structure of optimal consumption plans is described explicitly for a large class of aggregators. For Epstein-Zin preferences, the solution coincides with the solution of the corresponding time-additive preferences.

Zero-carbon-driven multi-energy coordinated sharing model for building cluster

The energy sharing among different intelligent buildings can improve renewable energy consumption and reduce carbon emission. However, the evaluation of the low carbon value of energy sharing is insufficient. Considering the different percentage of generated power of energy production units on the energy supply side during different periods, this study is the first to use time-of-use carbon emission evaluation factor to calculate the carbon emissions generated by buildings purchasing energy from upper-level networks. Then, an incentive-based carbon reward and punishment model is proposed based on the evaluation model. Meanwhile, operation decision models for different types of intelligent buildings are proposed. The optimal strategies for peer-to-peer energy sharing for intelligent buildings are obtained by the distributed algorithm. Results show that (1) The time-of-use carbon emission evaluation factor can facilitate the change of equipment output and energy consumption plans in buildings, and further improve the benefits of multi-energy sharing. (2) The proposed incentive-based carbon reward and punishment method shows greater advantages in reducing carbon emissions of high carbon emission buildings compared to the traditional carbon reward and punishment method. Although the benefits of some low carbon emission buildings with the proposed method may be sacrificed, CO2 emissions reduce by 7% for the entire building cluster.

Regulating Poverty: A Critical Discourse Analysis of Media Reporting on Energy Elite

The current discursive development of renewable energy in news media is toward a direction of hyper-commodification, in which the process of creating a commodity is mainly subject to dominant political ideologies rather than a free market. Regarding the process of media reporting on global leaders’ renewable energy policies to the global public, I utilized semantic network analysis and discourse historical analysis to explore the process of hyper-commodification in media narratives. I found that media discourse constructs an illusion when representing renewable energy as essential and affordable in people’s everyday lives and indeed portrays a future without poverty. The underlying statement is that renewable energy is the future if you can afford it. Such discourses reimagine commodification by producing an emergent social class and intensifying the ongoing class warfare through the use of language. Although seeking solutions to implementing renewable energy is key to the public interest, the urgency of having a sustainable plan for energy consumption is often omitted from current media discourse. Instead, such discourses that represent the social reality retrospectively transition from reflecting public interests to only those of well-limited social groups can lead to detrimental consequences for successfully implementing sustainable goals in a timely manner.

Consumption-investment decisions with endogenous reference point and drawdown constraint

We study a consumption-investment decision problem related to the past spending maximum. In the problem, we consider two crucial consumption levels: the lowest constrained level and a reference level, and both levels are fractions of the past spending maximum. The decision-maker has different risk aversions on different sides of the reference level. We solve this stochastic control problem and derive semi-explicit forms of the value function, optimal consumption plan, and optimal investment strategy. We find five important wealth thresholds which are nonlinear functions of the past spending maximum. Based on numerical results and theoretical analysis, we also find that the model has significant economic implications. There are at least three important predictions: the marginal propensity to consume out of wealth is generally decreasing but can be increasing for intermediate wealth levels, and it varies inversely with risk aversion at the reference level; the implied relative risk aversion is roughly a smile in wealth; the welfare is much more vulnerable to wealth shocks when the reference level is not reached.

Credit Markets with Time‐Inconsistent Agents and Strategic Loan Default

AbstractWe study credit contracts under a life‐cycle setting where time‐inconsistent agents lack the internal commitment to stick to consumption plans and external commitment to repaying loans. With unrestricted credit, agents with only internal commitment problems may overborrow. If, additionally, they face external commitment problems, lenders endogenously impose borrowing limits similar to the ability‐to‐repay rules consumer financial protection agencies impose. Even with restricted credit access, except in exceptional cases, agents suffering from the twin commitment problems can achieve, at most, fully sophisticated allocations. The government can achieve the first‐best allocations if and only if it is assisted with endogenously imposed borrowing limits.

Forecasting oil, coal, and natural gas prices in the pre-and post-COVID scenarios: Contextual evidence from India using time series forecasting tools

Stock market price prediction is considered a critically important issue for designing future investments and consumption plans. Besides, given the fact that the COVID-19 pandemic has adversely impacted stock markets worldwide, especially over the past two years, investment decisions have become more challenging for risky. Hence, we propose a two-phase framework for forecasting prices of oil, coal, and natural gas in India, both for pre-and post-COVID-19 scenarios. Notably, the Autoregressive Integrated Moving Average, Simple Exponential Smoothing, and K- Nearest Neighbor approaches are utilized for analyses using data from January 2020 to May 2022. Besides, the various outcomes from the analytical exercises are matched with root mean squared error and mean absolute and percentage errors. Overall, the empirical outcomes show that the Autoregressive Integrated Moving Average method is appropriate for predicting India's oil, coal, and natural gas prices. Moreover, the predictive precision of oil, coal, and natural gas in the pre-COVID-19 period seems to be better than in that the post-COVID-19 stage. Additionally, prices of these energy resources are forecasted to increase through the year 2025. Finally, in line with the findings, significant policy recommendations are made.

Two-Stage Low-Carbon Economic Dispatch of Integrated Demand Response-Enabled Integrated Energy System with Ladder-Type Carbon Trading

Driven by the goal of “carbon neutrality” and “emission peak”, effectively controlling system carbon emissions has become significantly important to governments around the world. To this end, a novel two-stage low-carbon economic scheduling framework that considers the coordinated optimization of ladder-type carbon trading and integrated demand response (IDR) is proposed in this paper for the integrated energy system (IES), where the first stage determines the energy consumption plan of users by leveraging the price-based electrical-heat IDR. In contrast, the second stage minimizes the system total cost to optimize the outputs of generations with consideration of the uncertainty of renewables. In addition, to fully exploit the system’s emission reduction potential, a carbon trading cost model with segmented CO2 emission intervals is built by introducing a reward-penalty ladder-type carbon trading mechanism, and the flexible thermal comfort elasticity of customers is taken into account by putting forward a predicted mean vote index on the load side. The CPLEX optimizer resolves the two-stage model, and the study results on a modified IES situated in North China show the proposed model can effectively reduce carbon emissions and guarantee economical efficiency operation of the system.

Retracted: Investigating the Impact of Bank Housing Credit Risk Control Strategy by Blockchain Technology on the Household Consumption Plan.

[This retracts the article DOI: 10.1155/2022/7021384.].

Using machine learning to determine acceptable levels of groundwater consumption in Iran

Groundwater footprint index (GFI) is an essential indicator to assess the sustainability of groundwater aquifers. Prediction of future GFI can significantly help managers and decision-makers of groundwater supply to better plan for future resilient consumption of surface and groundwater. In this context, artificial intelligence and machine learning models can aid to predict GFI in view of lacking or uncertain data. We used this technique to predict GFI for 178 Iranian aquifers. To our knowledge, this is the first time that GFI was predicted using machine learning models. Four models, i.e., adaptive neuro-fuzzy inference system, least-squares support vector regression, random forest, and gene expression programming, were used to predict GFI. Systematic combinations of eight variables, including precipitation, recharge, return water, infiltration from the river to the aquifer, groundwater exploitation, aquifer area, evaporation, and river drainage from the aquifer were used in the form of nine input scenarios for GFI prediction. The results showed that inclusion of all input variables gave the best results for predicting the GFI. Predicted GFIs were generally between 0.5 and 8 with an average of 1.9. A value above 1 indicates that groundwater consumption is not resilient that can adversely affect available groundwater resources in the future. Over-use of groundwater can lead to land subsidence. Especially, aquifers located in Qom, Qazvin, Varamin, and Hamedan provinces of Iran may be affected due to large over-use. Among the four models, least-squares support vector regression resulted in the highest prediction performance. Due to the poor performance of adaptive neuro-fuzzy inference system, the novel Harris hawks optimization algorithm was used to improve the performance of adaptive neuro-fuzzy inference system. The Harris hawks optimization - adaptive neuro-fuzzy inference system hybrid model improved the GFI prediction performance. Machine learning methods improve prediction of GFI for aquifers and thus, can be used to better manage groundwater in areas with less reliable data.

IQ, Expectations, and Choice

Abstract We use administrative and survey-based micro data to study the relationship between cognitive abilities (IQ), the formation of inflation expectations, and the consumption plans of a representative male population. High-IQ men display 50 $\%$ lower forecast errors for inflation than other men. High-IQ men, but not others, have consistent inflation expectations and perceptions over time. In terms of choice, only high-IQ men increase their consumption propensity when expecting higher inflation as the consumer Euler equation prescribes. Education levels, income, other expectations, and socio-economic status, although important, do not explain the variation in expectations and choice by IQ. Recent modelling attempts to incorporate boundedly rational agents into macro models do not fully capture all the facts we document. We discuss which dimensions of expectations formation and choice are important for heterogeneous-agents models of household consumption and for the transmission of fiscal and monetary policy.

Duality for optimal consumption under no unbounded profit with bounded risk

We give a definitive treatment of duality for optimal consumption over the infinite horizon, in a semimartingale incomplete market satisfying no unbounded profit with bounded risk (NUPBR). Rather than base the dual domain on (local) martingale deflators, we use a class of supermartingale deflators such that deflated wealth plus cumulative deflated consumption is a supermartingale for all admissible consumption plans. This yields a strong duality, because the enlarged dual domain of processes dominated by deflators is naturally closed, without invoking its closure. In this way, we automatically reach the bipolar of the set of deflators. We complete this picture by proving that the set of processes dominated by local martingale deflators is dense in our dual domain, confirming that we have identified the natural dual space. In addition to the optimal consumption and deflator, we characterise the optimal wealth process. At the optimum, deflated wealth is a supermartingale and a potential, while deflated wealth plus cumulative deflated consumption is a uniformly integrable martingale. This is the natural generalisation of the corresponding feature in the terminal wealth problem, where deflated wealth at the optimum is a uniformly integrable martingale. We use no constructions involving equivalent local martingale measures. This is natural, given that such measures typically do not exist over the infinite horizon and that we are working under NUPBR, which does not require their existence. The structure of the duality proof reveals an interesting feature compared with the terminal wealth problem. There, the dual domain is L1-bounded, but here the primal domain has this property, and hence many steps in the duality proof show a marked reversal of roles for the primal and dual domains, compared with the proofs of Kramkov and Schachermayer (Ann. Appl. Probab. 9 (1999) 904–950; Ann. Appl. Probab. 13 (2003) 1504–1516).

МОДЕЛИРОВАНИЕ РЕЖИМОВ РАСПРЕДЕЛЕНИЯ ВОДНЫХ РЕСУРСОВ МЕЛИОРАТИВНЫХ СИСТЕМ ДВОЙНОГО РЕГУЛИРОВАНИЯ В КОНТУРЕ «ГРУППА ВОДОПОТРЕБИТЕЛЕЙ»

The aim of the research was to develop an algorithm for modeling the regulation of water resources in the fields of reclamation systems for dual regulation of the water regime of soils (drainage-moistening, drainage-irrigation) in the «group of water consumers» contour (crop rotation plot) for the subsequent development of information and software tools that provide the possibility of operational regulation of water resources on dual regulation systems. The information and methodological basis of work related to modeling the modes of distribution of water resources in crop rotation plots was made up of methods for collecting and processing information, systematization methods, regulatory and technical documents, provisions for the development of information technologies, as well as existing means of information technology support for tasks in land reclamation. The task of modeling was to determine the modes of water supply from a water source (regulating tank, water intake-water source) by a pumping station by controlling the on and off of pumping units of different capacities available on it under conditions of rapidly changing water intake modes in the circuits regulation «Water consumers». The algorithm for modeling water regimes in the control loop «Group of water consumers» (crop rotation area) in the reclamation SDR includes three stages: preparation – input of initial data on water consumption plans in the control loops «Water consumer»; modeling – cost calculation; simulation results – total volumes of water to be supplied to the site, total volumes of water from the control tank or water-receiver-water source, and others. The developed algorithm makes it possible to quickly regulate the modes of water supply from a control tank or a water-receiver-water source by pumping stations by controlling pumping and power units under conditions of rapidly changing water intake modes in the «Water consumer» control loops.

On potential Pareto gains from free trade areas formation

This paper extends the results of Grinols and Silva to free trade areas with rules of origin requiring a minimum national content. In our model, producers of a member country may decide not to comply with rules of origin and to pay tariffs to export to other members. To ensure trade gains, firms in any member country must be allowed to export to other members paying tariffs not higher than before signing the agreement if they do not comply with rules of origin. The specific tariff revenue is rebated lump-sum to consumers, ensuring they can afford their status quo consumption plans.

Investigating the Impact of Bank Housing Credit Risk Control Strategy by Blockchain Technology on the Household Consumption Plan.

It is essential to solve the problem of information asymmetry in bank housing credit management, thus reducing bank operating costs and credit risk. Therefore, the present work constructs a bank housing credit blockchain alliance system based on the technical characteristics of blockchain to strengthen bank housing credit risk control. First, according to blockchain's elements and system architecture, the application of blockchain to bank housing credit business is analyzed. On this basis, the bank housing credit risk control strategy is proposed. Finally, the mathematical model is used to investigate residents' household consumption to explore the impact of housing credit on the household consumption plan of residents. The results show that when the educational level of the head of household is illiterate or semi-illiterate, the coefficients of participation credit (PC), nonparticipation credit (NPC), and total samples (TSs) of total household consumption are −0.064, 0.067, and 0.174, respectively; when the educational level of the head of household is primary school or junior middle school, the coefficients of PC, NPC, and TS of total household consumption are -0.026, 0.017, and 0.105, respectively; when the educational level of the head of household is high school or above, the coefficients of PC, NPC, and TS of total household consumption are 0.084, 0.073, and 0.064, respectively. The results indicate that the education level of the head of household plays a crucial role in the family consumption plan. Those with a high educational level will compromise on the best consumption decision after constantly weighing the consumption motivation and consumption thinking after obtaining the credit funds. The content reported here is vitally significant in guiding households to clarify the proportion of credit fund distribution and understand their consumption tendency.

A novel deep learning based peer‐to‐peer transaction method for prosumers under two‐stage market environment

With the development of the electricity market, peer-to-peer (P2P) transaction plays an important role in promoting local consumption of renewable energy and arousing the enthusiasm of prosumers. However, due to the diversification of prosumers, the confidentiality of the information and the interaction between prosumers, there are increasing challenges for the traditional model-based optimisation methods in both P2P modelling and model solution accuracy. Therefore, this paper proposes a novel P2P transaction method based on deep learning under a two-stage market environment, which uses a data-driven approach to build a transaction behaviour model based on public information. The neural network model based on Long Short-Term Memory (LSTM) is utilised to characterise the behaviour of prosumers in P2P transactions effectively. Based on this model, the energy consumption plans and P2P bids of prosumers are optimised accordingly. Through the simulation test of an example system with six prosumers, the results show that the model established can well represent the P2P transaction behaviour of prosumers, and the proposed method can effectively improve the efficiency of P2P transactions and the economic benefits of prosumers, providing a reference for the decision-making of P2P transactions.