Smart home energy management: An analysis of a novel dynamic pricing and demand response aware control algorithm for households with distributed renewable energy generation and storage

Home energy management systems (HEMS) technology can provide a smart and efficient way of optimising energy usage in residential buildings. One of the main goals of the Smart Grid is to achieve Demand Response (DR) by increasing end users’ participation in decision making and increasing the level of awareness that will lead them to manage their energy consumption in an efficient way. This research presents an intelligent HEMS algorithm that manages and controls a range of household appliances with different demand response (DR) limits in an automated way without requiring consumer intervention. In addition, a novel Multiple Users and Load Priority (MULP) scheme is proposed to organise and schedule the list of load priorities in advance for multiple users sharing a house and its appliances. This algorithm focuses on control strategies for controllable loads including air-conditioners, dishwashers, clothes dryers, water heaters, pool pumps and electrical vehicles. Moreover, to investigate the impact on efficiency and reliability of the proposed HEMS algorithm, small-scale renewable energy generation facilities and energy storage systems (ESSs), including batteries and electric vehicles have been incorporated. To achieve this goal, different mathematical optimisation approaches such as linear programming, heuristic methods and genetic algorithms have been applied for optimising the schedule of residential loads using different demand side management and demand response programs as well as optimising the size of a grid connected renewable energy system. Thorough incorporation of a single objective optimisation problem under different system constraints, the proposed algorithm not only reduces the residential energy usage and utility bills, but also determines an optimal scheduling for appliances to minimise any impacts on the level of consumer comfort. To verify the efficiency and robustness of the proposed algorithm a number of simulations were performed under different scenarios. The simulations for load scheduling were carried out over 24 hour periods based on real-time and day ahead electricity prices. The results obtained showed that the proposed MULP scheme resulted in a noticeable decrease in the electricity bill when compared to the other scenarios with no automated scheduling and when a renewable energy system and ESS are not incorporated. Additionally, further simulation results showed that widespread deployment of small scale fixed energy storage and electric vehicle battery storage alongside an intelligent HEMS could enable additional reductions in peak energy usage, and household energy cost. Furthermore, the results also showed that incorporating an optimally designed grid-connected renewable energy system into the proposed HEMS algorithm could significantly reduce household electricity bills, maintain comfort levels, and reduce the environmental footprint. The results of this research are considered to be of great significance as the proposed HEMS approach may help reduce the cost of integrating renewable energy resources into the national grid, which will be reflected in more users adopting these technologies. This in turn will lead to a reduction in the dependence on traditional energy resources that can have negative impacts on the environment. In particular, if a significant proportion of households in a region were to implement the proposed HEMS with the incorporation of small scale storage, then the overall peak demand could be significantly reduced providing great benefits to the grid operator as well as the households

Mathematical optimization techniques for demand management in smart grids

The electricity supply industry has been facing significant challenges in terms of meeting the projected demand for energy, environmental issues, security, reliability and integration of renewable energy. Currently, most of the power grids are based on many decades old vertical hierarchical infrastructures where the electric power flows in one direction from the power generators to the consumer side and the grid monitoring information is handled only at the operation side. It is generally believed that a fundamental evolution in electric power generation and supply system is required to make the grids more reliable, secure and efficient. This is generally recognised as the development of smart grids. Demand management is the key to the operational efficiency and reliability of smart grids. Facilitated by the two-way information flow and various optimization mechanisms, operators benefit from real time dynamic load monitoring and control while consumers benefit from optimised use of energy. In this thesis, various mathematical optimization techniques and game theoretic frameworks have been proposed for demand management in order to achieve efficient home energy consumption scheduling and optimal electric vehicle (EV) charging. A consumption scheduling technique is proposed to minimise the peak consumption load. The proposed technique is able to schedule the optimal operation time for appliances according to the power consumption patterns of the individual appliances. A game theoretic consumption optimization framework is proposed to manage the scheduling of appliances of multiple residential consumers in a decentralised manner, with the aim of achieving minimum cost of energy for consumers. The optimization incorporates integration of locally generated and stored renewable energy in order to minimise dependency on conventional energy. In addition to the appliance scheduling, a mean field game theoretic optimization framework is proposed for electric vehicles to manage their charging. In particular, the optimization considers a charging station where a large number of EVs are charged simultaneously during a flexible period of time. The proposed technique provides the EVs an optimal charging strategy in order to minimise the cost of charging. The performances of all these new proposed techniques have been demonstrated using Matlab based simulation studies

Distributed renewable energy integration using distribution locational marginal pricing and the food-energy-water nexus

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringHongyu WuAn increase in distributed energy resources (DERs), particularly non-dispatchable variable renewable energy (VRE) sources such as rooftop photovoltaic and small-scale wind turbines, brings about new challenges to distribution system operators. DERs, such as VRE sources, battery energy storage systems, distributed (conventional) generators, and responsive loads, increase the complexity of the distribution system. Therefore, system characteristics shift away from the previously passive system toward an active distribution system. With an active distribution system, there comes a need for fair and transparent pricing schemes, which reward DERs for reducing losses, voltage violations, congestion, and imbalance of lines. Further, increased energy storage capabilities and demand-responsive loads will help integrate the distributed resources by instantaneously balancing generation and demand. These balancing actions must also be rewarded. Therefore, a distribution locational marginal price (DLMP) mechanism serving as a price signal for the economic dispatch of generation sources within the distribution system is first proposed. Defined as the marginal cost to supply the next increment of power to a specific location, this mechanism may encourage the acceptance of DERs due to the incentives arising from nodal pricing. DLMP components for energy, loss, voltage violation, and congestion for a linear approximation of the alternating current optimal power flow are leveraged in this work. The proposed method also addresses VRE uncertainty using the data-driven probability efficient point method. Numerical results show the DLMP mechanism can serve as a tool to improve distribution grid conditions by encouraging or discouraging real and reactive power consumption at specific nodes in the system. It is also demonstrated that three-phase real and reactive nodal pricing allows for better control of diverse DERs in active distribution systems. The DLMP is further used in a home energy management system application that utilizes the blockchain for secure communication. Next, an investigation into the impact of coupling the electricity distribution and drinking water networks on the DLMP and efficient VRE integration is undergone. Water pumps serve as demand-responsive loads that follow the available VRE generation. This technique is achieved by using elevated water tanks to optimally schedule the water network operation to meet water demands while improving grid conditions. A novel linear coordinated water and energy model is formulated and validated on a coupled electrical distribution system and water network. Results show the impact of coupling water and energy networks on the cost of operation and the DLMPs. The inclusion of water tanks as alternative storage devices in the electricity distribution network are shown to moderately reduce voltage violations, line congestion, and VRE curtailments in a case with high VRE penetration. Finally, unique demand response and storage solutions are identified within an agricultural community microgrid that considers an electricity-run green ammonia synthesis plant. The small-scale ammonia plant's operational schedule follows the available VRE generation to reduce VRE curtailment and improve grid conditions. Excess renewables in the system can be stored as chemical energy in anhydrous ammonia. When the price of electricity is extremely high, the proposed model accounts for a direct ammonia fuel cell that consumes ammonia to provide electricity back to the grid. This work proposes a linear coordinated operational model of an electricity distribution system and an electricity-run, green ammonia plant. Case studies are performed on an agricultural community microgrid. Results indicate the ammonia plant can adequately serve as a demand response resource and positively impact the DLMP. Studies showed this coupling decreased electricity costs of the ammonia plant by nearly a third, with ammonia profits increasing 17%. This dissertation can serve as a tool for utilities implementing the DLMP market mechanism in distribution systems. It can further assist with the operation of coordinated operation of water and electricity distribution networks under uncertain VRE generation. Finally, agricultural community microgrid operators can utilize techniques proposed in this dissertation with the hope of increasing the vitality of small towns and rural communities

Demand response optimization for smart home scheduling under real-time pricing

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Efficient economic energy scheduling in smart cities using distributed energy resources

Machine learning provides a powerful mechanism to enhance the capabilities of the next generation of smart cities. Whether healthcare monitoring, building automation, energy management, or traffic management, use cases of capability enhancement using machine learning have been significant in recent years. This paper proposes a modeling approach for scheduling energy consumption within smart homes based on a non-dominated sorting genetic algorithm (NSGA). Distributed energy management plays a significant role in reducing energy consumption and carbon emissions as compared to centralized energy generation. Multiple energy consumers can schedule energy-consuming household tasks using home energy management systems in coordination to reduce economic costs and greenhouse gas emissions. In this work, such a home energy management system is used to collect energy price data from the electricity company via an embedded device-enabled smart meter and schedule energy consumption tasks based on this data. We schedule daily power consumption tasks using a multiobjective optimization method that considers environmental and economic sustainability. Two conflicting objectives are minimizing daily energy costs and reducing carbon dioxide emissions. Based on electricity tariffs, CO2 intensity, and the window of time during which electricity is consumed, energy consumption tasks involving distributed energy resources (DERs) and electricity consumption are scheduled. The proposed model is implemented in a model smart building consisting of 30 homes under 3 pricing schemes. The energy demand is spread out across a 24-hour period for points A2–A4 under CPP-PDC, which produces a more flattened curve than point A1. There are competing goals between electricity costs and carbon footprints at points B2–B4 under the CPP-PDC, where electricity demand is set between 20:00 and 0:00. Power grids’ peak energy demand is comparatively low when scheduling under CPP-PDC for points A5 and B5. Reducing carbon emissions, CPP-PDC reduces the maximum demand for electricity from the grid and the overall demand above the predetermined level. The maximum power demand from the grid is minimized for points A5 and B5, reducing up to 22% compared to A2. The proposed method minimizes both energy costs as well as CO2 emissions. A Pareto curve illustrates the trade-off between cost and CO2 emissions