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

A land evaluation model for irrigated crops using multi-criteria analysis.

This thesis investigated the optimal land suitability for irrigated crop production of barley and wheat in Benghazi region of Libya using multi-criteria analysis (MCA) of fuzzy logic and the Analytical Hierarchy Process (AHP). In the MCA, fourteen land suitability factors including twelve soil characteristics, topography and erosion hazard were evaluated. Local experts used their experience and assigned different weights based on crop requirements through pairwise comparison matrix. The combination of these methods was aimed at developing existing land evaluation model in the study area that was based on Boolean logic. Three models were developed based on Food and Agriculture Organization Framework: Model 1 was based on existing land evaluation model of Boolean and equal weights; Model 2 was based on Boolean but with difference in weights assigned using AHP; and Model 3 was based on Fuzzy and AHP. The results of these models were compared using crosstab classification (Kappa statistic and overall agreement). On comparison, Model 2 and Model 3 demonstrated higher agreement in spatial distribution of land suitability class than Model 1 for both barley and wheat crops. However, Model 3 is more realistic than the other two models when tested by linear regression. This implies that the application of fuzzy logic and AHP in MCA produces areas that are most suitable for barley and wheat production than would other methods. In practice, however, land management practices by farmers may lead to different yield in the selected suitable area. This thesis makes original contributions in the field of identifying the most suitable land evaluation model for application to crop production improvements. Furthermore, the results of this research will be useful to the Libyan authorities in planning for the optimisation of available land-use for strategic production of barley and wheat crops. This is pertinent to issues of food security. The approaches are transferable to other regions of the world which face similar challenges in domestic food production

A novel techno-economic multi-level optimization in home-microgrids with coalition formation capability

In recent years, microgrids (MG's) have operated in the power systems for various reasons such as reduction of energy losses, improvement of voltage stability and grid reliability. The implementation of Home Microgrid (H-MG) has proven successful in tackling these issues. This paper proposes a novel techno-economic multi-level optimization method and modern time varying price model aimed at encouraging participation in a coalition system, minimizing energy cost of a Home Microgrid (H-MG) and investigate the impact it has on voltage stability and reliability of the grid. The intended H-MG includes an apartment with several units which consist of electrical and thermal energy generators, energy storage devices and can trade energy within the H-MG's and the upstream network. The proposed method develops an algorithm for smart charging/discharging of energy storage and electric vehicles (EV) to improve energy efficiency. The performance of the proposed algorithm is tested on several electrical and thermal loads configurations, the IEEE 15 and 33-bus networks are used to prove the efficiency of the coalition system between the H-MG on a large scale. The simulations are implemented on MATLAB software and results indicate an improvement in voltage profiles and grid reliability