Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks

Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.

Price-Based Residential Demand Response Management in Smart Grids:A Reinforcement Learning-Based Approach

This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involved.The PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior.The challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM.To address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’responses.Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price models.The comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment.

Demand side management for solving environment constrained economic dispatch of a microgrid system using hybrid MGWOSCACSA algorithm

Microgrids are a type of restricted power distribution systems in which electricity is generated,transmitted,and distributed within a small geographic region.They are used to ensure that renewable energy sources are used to their full potential.Microgrids provide further benefits,such as lowering transmission losses and the expenses associated with them.This research compares and contrasts the aims of economic dispatch,emission dispatch,fractional programing based combined economic emission dispatch,and environmental restricted economic dispatch(ECED).A low-voltage microgrid system is investigated for three different scenarios.As a study optimization tool,an innovative,resilient,and strong hybrid swarm-intelligence optimization algorithm is utilised,which is based on combining the properties of the traditional grey-wolf optimiser,sine-cosine algorithm,and crow search algorithm.The employment of a time-of-use energy mar-ket pricing approach instead of a fixed pricing plan resulted in a 15%decrease in gen-eration costs throughout the course of the research.When ECED was assessed with a 15%-20%demand side management based restructured load demand model for the microgrid system,the generation costs were reduced even further.

Demand Side Management for Thermally Activated Building Systems Based on Multiple Linear Regression

The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems （TABS） based on multiple linear regression （AMLR） presented in this paper enables the application of demand side management （DSM） strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.

Internet of Things for Demand Side Management

The introduction of new kinds of energy mixes to the electricity grid is a challenging environmental task for present and future generations as they fight the pollution and global warming issues associated with urbanization. Individual appliances and whole buildings that continuously incorporate local intelligence which originates from the new technologies of Internet of Things are the new infrastructure that this integration is based on. Smart Electricity Grids are becoming more intensively integrated with tertiary building energy management systems and distributed energy generators such as wind and solar. This new smart network type harnesses the loT （lnternet of Things） principles by generating a new network made of active elements combined with the necessary control and distributed coordination mechanisms. This new self-organized overlay network of connected DER （distributed energy resources） allows for the seamless management and control of the active grid as well as the efficient coordination and exploration of single and aggregated technical prosumer potential （generation and consumption） to participate in energy balancing and other distributed grid related services, applying energy management strategies based on control and predict of the DERs behavior for facing demand side management issues.

Energy Planning in Small Municipalities Based on Monitoring Results and Demand Side Management

Recent estimates state that the European Union is on course to achieve only half of the 20% energy consumption reduction target by 2020. As the first governmental stakeholders involved in the implementation of energy saving initiatives, municipalities play a strategic role in the energy planning process. This paper focuses on establishment of an energy planning methodology for small municipalities with numbers of inhabitants in range of 1,000-10,000 which often face common problems associated with low efficient district heat supply systems and decreasing energy consumption in buildings. Particular attention is paid to DSM （demand side management） activities. DSM scheme includes legislative and financial flows with small investments from municipality side. Based on increased information and motivation it promotes reduction of energy consumption in all kinds of buildings. Practical experience has shown that application of DSM measures allows achieving 20% energy savings in municipal buildings during the first year.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Prediction-based Manufacturing Center Self-adaptive Demand Side Energy Optimization in Cyber Physical Systems

Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

Impact of Urban Water Pricing on Future Water Demand： A ＇Socioeconomic＇ Study in Greece

The main aim of this study is to assess various aspects of the current water policy, investigate the perspectives of water saving, evaluate water price elasticity and explore new approaches toward sustainable water management in the water sector, through a questionnaire survey that has been performed in the city of Volos, Greece, concerning the residential sector. The appropriate design of water management measures presupposes the investigation of the influence of some selected variables to consumers＇ behavior. The price of water, the size of the dwelling, the indoor and outdoor uses, the educational level, the income of consumers as well as rainfall and temperature levels are examined, the residential water demand curve is estimated and projections of future water demand under different pricing policies are performed.

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

Secure and efficient prediction of electric vehicle charging demand usingα^(2)-LSTM and AES-128 cryptography

In recent years,there has been a significant surge in demand for electric vehicles(EVs),necessitating accurate prediction of EV charging requirements.This prediction plays a crucial role in evaluating its impact on the power grid,encompassing power management and peak demand management.In this paper,a novel deep neural network based onα^(2)-LSTM is proposed to predict the demand for charging from electric vehicles at a 15-minute time resolution.Additionally,we employ AES-128 for station quantization and secure communication with users.Our proposed algorithm achieves a 9.2%reduction in both the Root Mean Square Error(RMSE)and the mean absolute error compared to LSTM,along with a 13.01%increase in demand accuracy.We present a 12-month prediction of EV charging demand at charging stations,accompanied by an effective comparative analysis of Mean Absolute Percentage Error(MAPE)and Mean Percentage Error(MPE)over the last five years using our proposed model.The prediction analysis has been conducted using Python programming.

New Opportunity and Challenges on Integrated Water Supply and Water Demand Managements

在未来10年以及更长的一段时间,全球和中国的水资源管理面临着紧迫的挑战,这些挑战包括水管控、水与食物、水与自然、水与能源、水与健康以及与气候变化相适应的与水相关的跨国合作,所有这些都与区域和全球尺度水安全相关。为了应对这些挑战,无论在中国还是世界范围,水资源管理战略必须解决需水管理(WDM)和供水管理(WSM)这两个重要问题,以及需水管理和供水管理的联合管理问题。本文将站在一个全球高度上研讨这一紧迫问题。通过分析,建议如下:在地方一级进行关于供水和需水管理的知识转让和经验交流和实践;在国家一级制定完善促进供需水管理的指导方针,最终发展到流域和区域级别。由于大陆或次大陆存在很大的差异,具体的区域指导报告是必要的,其中包括:专门针对现有可利用水资源和水需求的评估(通过使用水文方法进行水资源的评估以及需水调查和预测);关于组织、机构、税收、培训和教育的供需水的联合管理,在全球范围起草一个联合国公约草案。并在联合国会议上强调跨国界流域管理和/或竞争用水情况下的管理和/或水匮乏的供水需水联合管理的重要性。参照上述列举的目标,这些目标可能侧重于4个方面:在地方和国家一级(短期)供水管理;在地方和国家一级(短期)需水管理;在国家一级(中期和长期)供水管理和需水管理联合管理:在国际/区域一级(中期和长期)供水管理和需水管理联合管理。

IRP/DSM and Sustainable Development

This paper introduces the energy consumption status in China, elaborate the affects of the unreasonable energy consumption structure on energy environment and sustainable development of economy. Simultaneously, it points out the solution, i.e., to implement integrated resources planning (IRP)/demand side management (DSM), and gives some recommendations on the way of implementing IRP/DSM.

Decentralized Demand Management Based on Alternating Direction Method of Multipliers Algorithm for Industrial Park with CHP Units and Thermal Storage

This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains.A demand management model for industrial park considering the integrated demand response of combined heat and power(CHP)units and thermal storage is firstly proposed.Specifically,by increasing the electricity outputs of CHP units during peak-load periods,not only the peak demand charge but also the energy charge can be reduced.The thermal storage can efficiently utilize the waste heat provided by CHP units and further increase the flexibility of CHP units.The heat dissipation of thermal storage,thermal delay effect,and heat losses of heat pipelines are considered for ensuring reliable solutions to the industrial park.The proposed model is formulated as a multi-period alternating current(AC)optimal power flow problem via the second-order conic programming formulation.The alternating direction method of multipliers(ADMM)algorithm is used to compute the proposed demand management model in a distributed manner,which can protect private data of all participants while achieving solutions with high quality.Numerical case studies validate the effectiveness of the proposed demand management approach in reducing peak demand charge,and the performance of the ADMM-based decentralized computation algorithm in deriving the same optimal results of demand management as the centralized approach is also validated.

Application of WEAP Simulation Model to Hengshui City Water Planning

Like many river basins in China, water resources in the Fudong Pai River are almost fully allocated. This paper seeks to assess and evaluate water resource problems using water evaluation and planning (WEAP) model via its application to Hengshui Basin of Fudong Pai River. This model allows the simulation and analysis of various water allocation scenarios and, above all, scenarios of users' behavior. Water demand management is one of the options discussed in detail. Simulations are proposed for diverse climatic situations from dry years to normal years and results are discussed. Within the limits of data availability, it appears that most water users are not able to meet all their requirements from the river, and that even the ecological reserve will not be fully met during certain years. But the adoption of water demand management procedures offers opportunities for remedying this situation during normal hydrological years. However, it appears that demand management alone will not suffice during dry years. Nevertheless, the ease of use of the model and its user-friendly interfaces make it particularly useful for discussions and dialogue on water resources management among stakeholders.

Emission Patterns under Alternative Congestion and Motor Vehicle Pollution Mitigation Policies in Shanghai

As a megacity with thriving economy, Shanghai is experiencing rapid motorisation and confronted with traffic congestion problems despite its low car ownership. It is of value to look into the policies on emission control of motor vehicle and congestion reduction in such a city to explore how to reconcile mobility enhancement with the environment. Results of a dynamic simulation displayed time paths of emissions from motor vehicles in Shanghai over the period from 2000 to 2020. The simulation results showed that early policies on emission control of motor vehicle could bring about far-reaching effects on emission reduc- tion, and take advantage of available low-polluting technologies and technical innovation over time. Travel demand management would play an important role in curbing congestion and reducing motor vehicle pollution by calming down car ownership rise and deterring inefficient trips as well as reducing fuel waste caused by congestion.

Travelers'attitudes toward carpooling in Lahore:motives and constraints

Traffic congestion has become a critical issue in developing countries,as it tends to increase social costs in terms of travel cost and time,energy consumption and environmental degradation.With limited resources,reducing travel demand by influencing individuals’ travel behavior can be a better long-term solution.To achieve this objective,alternate travel options need to be provided so that people can commute comfortably and economically.This study aims to identify key motives and constraints in the consideration of carpooling policy with the help of stated preference questionnaire survey that was conducted in Lahore City.The designed questionnaire includes respondents’ socioeconomic demographics,and intentions and stated preferences on carpooling policy.Factor analysis was conducted on travelers’ responses,and a structural model was developed for carpooling.Survey and modeling results reveal that social,environmental and economic benefits,disincentives on car use,preferential parking treatment for carpooling,and comfort and convenience attributes are significant determinants in promoting carpooling.However,people with strong belief in personal privacy,security,freedom in traveling and carpooling service constraints would have less potential to use thecarpooling service.In addition,pro-auto and pro-carpooling attitudes,marital status,profession and travel purpose for carpooling are also underlying factors.The findings implicate that to promote carpooling policy it is required to consider appropriate incentives on this service and disincentives on use of private vehicle along with modification of people’s attitudes and intentions.

Mobility driven network slicing: an enabler of on demand mobility management for 5G

As different requirements on mobility support will be introduced by diversified communication scenarios in the fifth generation （5G）, on demand mobility management is put forward to simplify signaling process, reduce terminal power consumption, improve network efficiency and so on. In order to enable on demand mobility management in 5G networks, a mobility driven network slicing （MDNS） was proposed, which takes individual mobility support requirements into account while customizing networks for different mobile services. Within the MDNS framework, the actual levels of required mobility support are determined by a mobility description system, and network slice templates with the corresponding mobility management schemes are defined by a network slice description function. By instantiating the network slices, each mobile terminal could be directed to the network slice with the most appropriate mobility management scheme. Based on this, a prototype was implemented to validate the feasibility of MDNS framework, i.e. creating multiple network slices with different mobility management schemes. In addition, the performance evaluation on average cost of processing a mobility event is conducted for the proposed MDNS framework and the long term evolution （LTE） system, and operating benefits are analyzed including efficiency and scalability.

Smart residential energy management system for demand response in buildings with energy storage devices

In the present scenario,the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation.Demand side management(DSM)is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives.Consumers are expected to respond(demand response(DR))in various ways to attain these benefits.Nowadays,residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals.In this paper,the use of a smart residential energy management system(SREMS)is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances.Further,the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery(charging/floating/discharging)and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit(CCL).The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.