Housing Development Building Management System (HDBMS) For Optimized Electricity Bills

Smart Buildings is a modern building that allows residents to have sustainable comfort with high efficiency of electricity usage. These objectives could be achieved by applying appropriate, capable optimization algorithms and techniques. This paper presents a Housing Development Building Management System (HDBMS) strategy inspired by Building Energy Management System (BEMS) concept that will integrate with smart buildings using Supply Side Management (SSM) and Demand Side Management (DSM) System. HDBMS is a MultiAgent System (MAS) based decentralized decision making system proposed by various authors. MAS based HDBMS was created using JAVA on a IEEE FIPA compliant multi-agent platform named JADE. It allows agents to communicate, interact and negotiate with energy supply and demand of the smart buildings to provide the optimal energy usage and minimal electricity costs. This results in reducing the load of the power distribution system in smart buildings which simulation studies has shown the potential of proposed HDBMS strategy to provide the optimal solution for smart building energy management.


I. INTRODUCTION
Electricity has become a basic necessity for modern society and is a vital part of a socio-economic development in the world. The common challenges faced by the electricity suppliers would be systems constraints and administrative issues while supplying electricity. Additionally, past studies had shown a steady increase in annual electricity demand through the past few years due to consumers needs with respect to comfort, convenience and flexibility. In order to meet the growing demand of electricity, solutions from grid distribution to end users are required [1].
Smart grid represents a innovative next generation of power system network that delivers power from supply to demand. It uses a two-way communications which leads to enhanced reliability and energy efficiency of the grid. It has the capabilities of sensing grid situations, measure power and control appliances to electricity generations, transmission and distribution of the power grid. This increased the number of decentralised renewable energy sources due to increasing electricity demand. These issues created challenges for a stable and secure operation of power grid. Major developments were based on traditional electrical system planning and operations with information and communications technology [2]. This concept of intelligent power grid performs independent adaptations of its elements for optimal electricity consumption [3]. Important elements of a smart grid includes demand response, load management and customer engagement. The accuracy of forecasting supply and demand depends on smart meters. The stability of grid in Singapore has one of the most reliable electricity networks in the world. Singapore grid has already deployed advanced Supervisory Control and Data Acquisition (SCADA) systems with two-way communication channels. This situation creates an ideal place to bring new technologies to enhance the capability of its power grid. With the help of advanced technologies, the grid was able to respond efficiently and effectively to power disruptions [4].
Building Energy Managements System is a part of a smart grid system that control, monitor and optimize energy for buildings. BEMS play a critical role in achieving overall energy efficiency by reducing carbon footprints. As it is a key requirement for designing modern buildings and industries. Technology improvement on control systems, energy managements systems, distributed decision making and coordination for buildings contribute to better efficiency of energy usage [5].
In modern times, BEMS considered improvement on energy utilization efficiency, reduction of energy cost and integration of renewable energy technology. These improvements was meant to meet the energy demand of the buildings. To align with the zero-energy objectives and intelligent building concepts, BEMS aims to increase energy efficiency by integrating Demand Side Management and Supply Side Management. These functions decreases energy cost and sustain consumer comforts by optimizing buildings electrical distribution. It will solve complex issues through coordination and cooperation of the management systems. This is due to the system being flexible, reliable and efficient [6].
Supply Side Management (SSM) was identified as a method of optimizing the electrical supplies from various power sources. One of the key issues for future energy distribution systems, smart buildings and smart devices was the need of an intelligent management of energy distributions. The problem can be tackled from the supply sources on how electricity should be distributed. It helps to improve electrical efficiency by developing an optimal algorithm for the system. Thus, electricity can be used in an efficient manner [7], [8].
Demand Side Management (DSM) was identified as a method of optimizing energy demand consumption to achieve better efficiency and operations in an electrical system. DSM usually involves demand response and peak load scheduling. As electricity was cheaper during night time due to the whole-monthly electricity consumption by public housing unit was 371 kWh [19]. The power utilization of frameworks, for example, regular territory lighting, lifts and water pumps of a normal HDB residential building was estimated to be 75,000 kWh a year [20].
According to a research done in 2015, the public housing buildings known as Housing and Development Board (HDB) blocks currently has 9503. This indicated that there were 980108 units of flats which averaged to 103 units per block [21]. This information builds a realistic situation for the experiment.

B. Singapore Electricity Prices
Following 2001, the Energy Market Authority (EMA) opened a retail electricity market to give consumers options to manage their energy cost through the competition of different providers. Instead of buying at regulated electricity tariff which is known as non-contestable price from SP Services Limited (SPS). Consumers were able to purchase electricity from electricity retailers or wholesale electricity market at prices that varies every half-hour which is known as contestable prices.
In order to switch to contestable prices, consumers with an average electricity consumption of 2000 kWh per month were eligible for the scheme [22].
The non-contestable consumers pricing will be 20.35 cents per kWh (with effect from 1 Oct 15 to 31 Dec 15) regardless of the time periods [23].
Data collected were used for accommodation of different pricing that allows research on whether power delivered from a contestable or non-contestable electricity source be more economical for residential building in Singapore. Fig.5 shows the price for a Contestable consumer in a 48 period format. It represents a day where each period is half an hour from Energy Market Company (EMC) for 1 September 2015 [24]. The capability of PV electricity generation in Singapore depends essentially on the space and the efficiency of the PV systems. The annual electricity demand in Singapore is 42 TWh in 2011; the total installed solar PV capacity was 4MWp for both residential and non-residential installation which generates about 4.8GWh per annum [25]. The PV system was able to yield 51kWh per day between 0700-1900 hours in Singapore.  Public Normal Chargers are typically introduced or accessible at shopping centers, HDB car parks, and charging stations while Residential Normal Chargers were found in private properties. Both sorts of chargers have a more extended charging time of 7 to 8 hours in contrast to quick chargers.

D. Singapore Electric Vehicle (EV)
The quick chargers are typically introduced or accessible at certain shopping centers and charging stations. It takes a quicker charging rate of 30 to 45 minutes as it will provide more electrical power.
For Electric Vehicle (EV) or hybrid car, charging stations are expected to energize the batteries for the vehicles which are about 24kW. There are presently 71 charging stations by Bosch in Singapore and buyers need to pay SGD$180 a month for unlimited charging [26].
According to the Straits time, there are a total of approximate 1.25 million households and around 45% of households in Singapore own a car. [27].
The number of electrical vehicle in a residential building can be calculated by:  The HDB non-contestable and contestable prices were constant at $5.11 and $1.65 throughout the periods.  Between contestable and non-contestable pricing, it shows significant difference of cost even when no system is implemented. After the system was implemented, it shows further reduction in cost with the set points methods. Although initially the set points method cost more but as the time increases, it shows slight difference with the average point system and a bigger difference with cheap set point by comparing the electricity cost of household.
At the same time, the per period simulations shows the system cost higher at later periods. This was due to "off-peak" prices availiable at later timings. However, it does not affect the overall results as it is implemented and compared with different methods.
These results concluded that with the Housing Development Building Management System (HDBMS) implemention, residential buildings were able to save a significant amount of electricity bills and reduce the load of electricity generators in the grid. HDBMS provides a more cost efficient way of using electricity.
VII. CONCLUSIONS In this paper, the development of a smart building concept was designed to optimize the electricity usage through a Housing Development Building Management System (HDBMS). This system aims to optimize the energy efficiency and electricity costs of the residential buildings. A universal idea of a HDBMS for smart buildings was introduced by integrating Demand Side Management (DSM) and Supply Side Management (SSM) system. Multi-Agent System (MAS) was used to illustrate the communication process of agents between management systems and devices for the calculation and data of energy sources. Such system has shown its capabilities to achieve the optimal use of energy efficiency and electricity bills.
Enhancements of such smart grid techniques would only benefit the power grid in terms of increased dependability and software improvement for the grid. A less costly electricity bill could also be achieved with the help of renewable energy resources and electricity market. Eventually, this methodology would genuinely be a step closer towards a reliable and decentralized decision making smart building system via effective optimization system.