Validation of a solid phase extraction technique for the determination of halogenated acetic acids in drinking water

Haloacetic acids (HAAs) are one of the most common disinfection by-products formed during chlorination of drinking water. An analytical method involving solid phase extraction (SPE) followed by gas-chromatograph mass-spectrometry (GC-MS) was developed and optimized using experimental design to determine the HAAs in water. Selectivity, percent recovery, and detection limit studies were carried out on a Silia-SAX (Trimethyl ammonium chloride) SPE. Under optimized conditions, average recoveries for nine HAAs spiked in drinking water samples range from 69.2% to 108.2 %. The relative standard deviation (RSD) data were found to range from 2.5 % to 12.5% based upon five repeat recovery experiments and detection limit range of 0.16 to 0.009μg/l were obtained. On this basis, SPE was studied as a possible alternative to liquid-liquid extraction (LLE) for the analysis of HAAs in water. The performance of the SPE-GC-MS with actual water samples was tested

Application of particle swarm optimization for solving optimal generation plant location problem

The global demand for energy especially-in-developing-countries,-has-been witnessing a tremendous growth due to rapid population growth, economic growth and developing industrial sectors. Therefore, it is necessary to forecast the future energy needs and expand generation capacity to meet the increasing peak demand.-This-paper-presents-an-optimization approach to determine the optimal location for installing a new generator in which the technical, economic and environmental aspects are all taken into consideration. The location that yields the minimum fuel costs, total emission and system loss is considered as the optimal generation plant location. The- formulated- objective- function- and- its constraints compose an optimization problem is solved using particle swarm optimization (PSO). The proposed PSO based optimization approach is tested on IEEE 14-bus system and IEEE 30-bus system to illustrate the potential of the new approach. The simulation results have shown that the proposed approach is indeed capable of determining the optimal generation location that can save much overall fuel cost as well as reduce the total emissions of generators and losses in the network

Pollution pattern of volatile organic compounds (VOCs) in drinking water using chemometrics data analysis methods

The occurrence of volatile organic compounds (VOCs) in drinking water create a major concern over the possible health risk because, even at very low concentrations, many of these materials are toxic,carcinogenic or mutagens (Sacks & Akard,1994). As considerable quantities of VOCs are manufactured in Malaysia, their use is certainly ubiquitous. VOCs are contained in many manufactured products, including paints, adhesives, gasoline, and plastics (Nikolaou et al., 2002)

Smart home appliances scheduling considering user comfort level

Smart home appliances scheduling, employing optimization optimization algorithms to reduce utility costs, is gaining traction under the introduction of time-of-use tariffs and the development of Internet of Things (IoT). The prior electricity cost reduction scheduling algorithms, however, causes substantial discomfort to users for restricting users from using the appliances at their desired times. To address the problem, a novel versatile systematic method is proposed by pricing the mismatch of proposed schedule with users' usage preference pattern to quantify discomfort, coupled with comfort-cost weight factor. The method employing customizable user preference patterns, user-perceived pricing of mismatch and user-specified comfort-savings weightage, not only captures the complex dependence of comfort to individual preference, but the evolution with time by continuous user survey. The proposed method, formulated to be simple enough to be applied on an Excel spreadsheet, demonstrates substantial reduction of electricity cost and users' discomfort simultaneously. Studies on the algorithm found it to be robust against of fluctuations of parameters, with optimization performance comparable to prior work. The work demonstrates that despite the complex nature of comfort to users' behaviors and perception, simple pricing surveys can be used to accurately quantify, compare and optimize users' comfort together with economic savings

Generator Revenue Adequacy in the Competitive Electricity Markets: The case of Malaysia

Malaysia, like many other developing countries, is reforming its electric supply industry into a more transparent, efficient and competitive environment. The introduction of Independent Power Producers (IPPs) in 1992 was the first step taken to encourage the private investors to participate in the generation sector. The adoption of the single buyer market model in 2001 was a step further to create competition in generation. However, these efforts invite the financial crisis due to IPP generation capacity price obligation and generation surplus as stated in the Power Purchase Agreement (PPA). As the PPA is coming towards the end, the pool market model was  initially identified as a possible model to overcome the weaknesses of the single buyer market. However, this model could invite a lot of denials from the power producers if it is not implemented properly. This paper proposes a hybrid market model to satisfy the generator revenue adequacy in Malaysian electricity markets under a competitive environment. A case study of Malaysia’s electricity market system is used to illustrate the proposed market. The result shows that the proposed market model has merit over a pool market model in the context of guaranteed revenue remuneration for each generator. The hybrid model proposed in this paper could effectively be used by ESI in developing countries as a first step of introducing a competition in their electricity marke

Comparison of new and previous Net Energy Metering (NEM) scheme in Malaysia

Net Energy Metering (NEM) scheme was introduced in Malaysia in 2016 to replace the previous Feed in Tariff (FIT) scheme. NEM allows electricity consumers to generate, use and export the net excess energy to the grid. For the net excess energy exported to the grid, the consumer will be paid base on the displaced cost per kWh unit. However, after two years of implementation, not many consumers engaged with the NEM scheme as compared to the previous FIT scheme due to the poor financial return. Beginning 2019, new NEM scheme (NEM 2019) is introduced to replace the previous NEM 2016 scheme. This paper will investigate the potential financial return of the new NEM 2019 in term of net present cost (NPC) and electricity cost savings. The analysis is conducted by using HOMER software on three different size of residential customers; large, medium and small. Different photovoltaic (PV) panel sizes ranging from 1kWp to 8kWp were used in the analysis. The results show that the NEM 2019 produced lower NPC as compared to NEM 2016 for most cases

Electricity consumption pattern disaggregation using non-intrusive appliance load monitoring method

In practice, a standard energy meter can only capture the overall electricity consumption and estimating electricity consumption pattern of various appliances from the overall consumption pattern is complicated. Therefore, the Non-Intrusive Appliance Load Monitoring (NIALM) technique can be applied to trace electricity consumption from each appliance in a monitored building. However, the method requires a detailed, second-by-second power consumption data which is commonly not available without the use of high specification energy meter. Hence, this paper analyzes the impact of different time sampling data in estimating the energy consumption pattern of various appliances through NIALM method. This is so that consumers will have an overview of time sampling data which is required in order to apply the NIALM technique. As for the analysis, air-conditioning systems and fluorescent lamps were used in the experimental setup. One minute sample rate was the minimum time interval required by NIALM carried out in this analysis. Through the study presented in this paper, it can be established that higher time sampling led to uncertain appliance detection and low accuracy

Disaggregated Electricity Bill Base on Utilization factor and Time-of-use (ToU) Tariff

Time of Use tariff is introduced to motivate users to change their electricity usage pattern. Commonly the tariff is high during peak hours and relatively low during off peak hours, to encourage users to reduce consumption during peak hours or shift it to off-peak hours. This tariff scheme provides opportunities for building owners to reduce their electricity bill provided that their electricity usage patterns of various spaces in that building at every hour are known. In practice, the kWh meter installed by the utility can only provide the overall hourly electricity consumption pattern. To know the usage pattern of different spaces or rooms, separate individual meter need to be installed in each space/room, which is costly and impractical.  This paper presented the disaggregated electricity bill method based on user utilization factor and time of use (ToU) tariff. It estimates hourly electricity bill of each appliance at each space/room. Utilization factor is used to represent the electricity usage behavior of the occupants. The proposed method is applied on practical load profile data of a university building

A multi-timescale hybrid stochastic/deterministic generation scheduling framework with flexiramp and cycliramp costs

Flexible ramping products (flexiramp), provided by entitled resources to meet net demand forecast error, are the underpinning for the accommodation of the substantial uncertainties associated with variable wind power. This paper proposes an enhanced flexiramp modeling approach, cast in a hybrid stochastic/deterministic multi-timescale framework. The framework employs a chance-constrained day-ahead scheduling method, as well as deterministic scheduling on intra-hourly basis (real-time scheduling), to allow optimal procurement planning of the flexiramp products in both timescales. A stepwise and piecewise demand price curve is also proposed to calculate the flexiramp surplus procurement price. Non-generation resource (NGR), referring to energy storage, is implemented to provide extra flexibility. Additionally, cycling ramping cost (cycliramp), introduced to model operational and maintenance costs and reduce the wear and tear of generators, is also included as a penalty. Numerical tests are conducted on 6-bus and 118-bus systems. Results demonstrate the merits of the proposed scheduling model as well as the effects of flexiramp and cycliramp costs in the multi-timescale scheduling. © 2018 Elsevier Lt

Chance-constrained Scheduling of Variable Generation and Energy Storage in a Multi-Timescale Framework

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting