Residential Energy Scheduling for Variable Weather Solar Energy Based on Adaptive Dynamic Programming

The residential energy scheduling of solar energy is an important research area of smart grid. On the demand side, factors such as household loads, storage batteries, the outside public utility grid and renewable energy resources, are combined together as a nonlinear, time-varying, indefinite and complex system, which is difficult to manage or optimize. Many nations have already applied the residential real-time pricing to balance the burden on their grid. In order to enhance electricity efficiency of the residential micro grid, this paper presents an action dependent heuristic dynamic programming(ADHDP) method to solve the residential energy scheduling problem. The highlights of this paper are listed below. First,the weather-type classification is adopted to establish three types of programming models based on the features of the solar energy. In addition, the priorities of different energy resources are set to reduce the loss of electrical energy transmissions.Second, three ADHDP-based neural networks, which can update themselves during applications, are designed to manage the flows of electricity. Third, simulation results show that the proposed scheduling method has effectively reduced the total electricity cost and improved load balancing process. The comparison with the particle swarm optimization algorithm further proves that the present method has a promising effect on energy management to save cost.

Comparison of Residential Energy Consumption in Different Geographical Areas and Relevance for Environmental Policy

Residential energy consumption is varying considerably worldwide. In order to understand these variations, and thus design effective policies for energy reductions, one needs a better understanding of the different drivers behind these variations. A comparison of geographical areas with similar climate and socio-economic conditions has shown significant variations in residential energy consumption across otherwise comparable case studies. This research paper aims to identify cultural and historical parameters that contribute to these significant variations, including economic, environmental and social traditions related to local availability of natural resources. Furthermore, the transferability of these parameters is evaluated, taking into consideration local legislation and planning documents, and historic and socio-economic accessibility of resources. It is evaluated to which degree these parameters can be transferred and included into building assessment tools and policy documents for planning and transformation of sustainable urban neighborhoods.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

A Data Analytics Study on Building Characteristics Impacting Energy Consumption in Single-Family Attached Homes

San Antonio, Texas is the seventh largest city in the United States with a population of 1.4 million people, and ranked among the fastest growing cities. To assess the implications of past and present building practices within the residential sector on future energy consumption, the energy utilization of single-family attached homes (SFAH) in Bexar County, Texas is studied. The available dataset includes 3932 SFAH records representing about 33% of the total number of SFAHs within the county. The study is based on pairing and analyzing data at the individual building level from a variety of sources including the buildings’ physical characteristics, access to fuels, and monthly energy consumption. The results indicate that the area of conditioned space, presence of swimming pools, number of stories, presence of fireplaces, fuel-type, and number of shared walls are a significant factor on the energy consumption of single-family attached homes. In terms of energy consumption, all-electric two-story homes sharing two walls are the most energy efficient among SFAHs. This study can aid comprehensive master planning efforts for developing sustainable communities by highlighting key features of SFAHs and making the case for higher density housing as a viable and more energy efficient alternative to single-family detached homes (SFDH).

Calculation and decomposition of regional household energy consumption in China:based on perspectives of urbanization and residents' consumption

Demographic urbanization caused great changes in scale of residents' consumption and residents' lifestyle and then impacted changes of regional household energy consumption. This paper expanded Logarithmic Mean Decomposition Index method through introducing variables of urbanization and residential consumption into the model. It also analyzed the influences of six factors as energy structure, energy intensity, population scale, urbanization, residential consumption, and consumption inhibit on regional household energy consumption. Results showed that in 2003-2012, impact of urbanization on regional household energy consumption of Chinese three areas was significantly higher than population size. The "population gathered in eastern region" phenomenon caused eastern region getting the largest population scale effect. Driving force of residential consumption on regional household energy consumption was much higher than the other five effects. Due to the comparative advantage of residential consumption compared with government consumption, investment, and net export, the decrease of consumption ratio promoted the growth of regional household energy consumption. Energy intensity in Chinese three regions kept reducing in 2003-2012. The progress of energy utilization technology slowed the growth of regional household energy consumption, and energy intensity effect was most significant in the central region.

Energy and Cost Analyses of Solar Photovoltaic (PV) Microgeneration Systems for Different Climate Zones of Turkey

The environmental and energy problems that have arisen in Turkey because of the dramatically increase in energy consumption require the implementation of energy efficiency and microgeneration measures in the building sector which is the main sector of primary energy consumption. Since Turkey is highly dependent on exported energy resources, the basic energy policy approach is based on providing the supply security. In this regard, supporting for in situ energy production, encouraging the use of renewable energy sources and the systems such as microgeneration systems in order to meet the energy requirements of buildings would be considered as a key measure for resolving the energy related challenges of Turkey and dealing with the sustainability issues. Turkey’s geographical location has several advantages for extensive use of most of the renewable energy sources such as especially solar energy. However, this huge solar energy potential is not being used sufficiently in residential building sector which is responsible for the great energy consumption of Turkey. Therefore, this paper aims to introduce a study which investigates, on a life cycle basis, the environmental and the economic sustainability of solar Photovoltaic (PV) microgenerators to promote the implementation of this system as an option for the renovation of existing residential buildings in Turkey. In this study, main parameters which were related to the distribution of modules to be installed in flat roofs and facades and the evaluation of the PV systems were taken into account. The effect of these parameters on energy generation of PV systems was analyzed in a case study considering different climate zones of Turkey;and the decrease in the existing energy consumption of the reference building was calculated.

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%.

INDOOR AIR QUALITY(IAQ)IMPROVEMENTS USING BIOFILTRATION IN A HIGHLY EFFICIENT RESIDENTIAL HOME

INTRODUCTION Americans spend the majority of their time indoors where levels of pollutants may run two to five times-and occasionally more than 100 times-higher than outdoor levels[1].Many of these pollutants can cause adverse health reactions in building occupants,which can contribute to lower worker productivity and increased sick leave.Traditional methods of indoor pollutant control in sealed buildings involve the use of outdoor ventilation.Outdoor ventilation requires the intake of outdoor air,which must be heated or cooled to meet indoor temperature and humidity requirements.This represents between 10-20%of the total energy consumption of a building[2].

Indoor environment of nearly zero energy residential buildings with conventional air conditioning in hot-summer and cold-winter zone

According to the few researches on Nearly zero energy residential buildings(NZERB)in hot-summer and cold-winter zone,although it could reduce the cooling load of buildings due to its high thermal insulation and air tightness,it still needed for certain cooling in summer.This paper studied indoor environment of NZERB un-der three kinds of air-conditioners(split-type air-conditioner,multi-line air-conditioner and ceiling radiant air-conditioner).Firstly,a simulation model of NZERB was established based on Nanjing,a typical city in hot-summer and cold-winter zone.Secondly,variation of indoor air temperature and building load characteristics with outdoor air temperature were studied.Thirdly,indoor environment and energy consumption under three selected con-ventional air-conditioners in summer were simulated.Finally,the discussion was given,and an air-conditioner combining with convective and radiant cooling were proposed.The results indicated that the air-conditioner needed to be turned on in NZERB in hot-summer and cold-winter zone due to the room air temperature in off-air condition ranged from 32℃to 36℃,which was higher than designed indoor environment temperature in sum-mer,but the indoor environment of NZERB under three selected conventional air-conditioners could not meet the requirements of energy saving and comfort at the same time,and a proposed convective-radiant air-conditioner could be fast,stable,and energy saving.The findings can provide a reference for conducting active technology in NZERB.

Cleaning the energy sources for water heating among Nanjing households： barriers and opportunities for solar and natural gas

Energy for water heating accounts for an increasing part in residential energy demand in China. An extensive survey was conducted to analyze the determinants of household energy choices for water heaters among residents in Nanjing, China. Two sets of variables were examined as potential influences： building features and household socio-economic characteristics. Results suggest that building features such as gas availability and building structures, and household characteristics such as household head＇s education degree and energy-conserving sense are crucial determinants in choosing natural gas as water heater energy. Installation permission for solar water heater, building stories, and residential location serve as determining factors in choosing solar water heaters. Based on these, barriers and opportunities are discussed for transitions toward cleaner water heating energies, and suggestions are given for local governments to promote cleaner energy replacement in China.

Deep reinforcement learning for home energy management system control

The use of machine learning techniques has been proven to be a viable solution for smart home energy man-agement.These techniques autonomously control heating and domestic hot water systems,which are the most relevant loads in a dwelling,helping consumers to reduce energy consumption and also improving their comfort.Moreover,the number of houses equipped with renewable energy resources is increasing,and this is a key ele-ment for energy usage optimization,where coordinating loads and production can bring additional savings and reduce peak loads.In this regard,we propose the development of a deep reinforcement learning(DRL)algorithm for indoor and domestic hot water temperature control,aiming to reduce energy consumption by optimizing the usage of PV energy production.Furthermore,a methodology for a new dynamic indoor temperature setpoint definition is presented,thus allowing greater flexibility and savings.The results show that the proposed DRL al-gorithm combined with the dynamic setpoint achieved on average 8%of energy savings compared to a rule-based algorithm,reaching up to 16%of savings over the summer period.Moreover,the users’comfort has not been compromised,as the algorithm is calibrated to not exceed more than 1%of the time out the specified temperature setpoints.Additional analysis shows that further savings could be achieved if the time out of comfort is increased,which could be agreed according to users’needs.Regarding demand side management,the DRL control shows efficiency by anticipating and delaying actions for a PV self-consumption optimization,performing over 10%of load shifting.Finally,the renewable energy consumption is 9.5%higher for the DRL-based model compared to the rule-based,which means less energy consumed from the grid.

Electricity Tariff Aware Model Predictive Controller for Customer Battery Storage with Uncertain Daily Cycling Load

To optimally control the energy storage system of the battery exposed to the volatile daily cycling load and electricity tariffs,a novel modification of a conventional model predictive control is proposed.The uncertainty of daily cycling load prompts the need to design a new cost function which is able to quantify the associated uncertainty.By modelling a probabilistic dependence among flow,load,and electricity tariffs,the expected cost function is obtained and used in the constrained optimization.The proposed control strategy explicitly incorporates the cycling nature of customer load.Furthermore,for daily cycling load,a fixed-end time and a fixed-end output problem are addressed.It is demonstrated that the proposed control strategy is a convex optimization problem.While stochastic and robust model predictive controllers evaluate the cost concerning model constraints and parameter variations.Also,the expected cost across the flow variations is considered.The density function of load probability improves load prediction over a progressive prediction horizon,and a nonlinear battery model is utilized.