Singapore Electricity Market Research Articles

Assessing ASEAN’s Liberalized Electricity Markets: The Case of Singapore and the Philippines

The efforts towards the liberalization of electricity markets have sped up recently in some countries within the Association of Southeast Asian Nations (ASEAN) region. This step of opening up the electricity markets is aimed at establishing competitive and efficient electricity markets that not only reduce electricity prices, but also support a sustainable future by reducing carbon dioxide (CO2) emissions from electricity generation and promoting the wider adoption of renewable energy (RE)-based electricity generation. This paper assesses the effects of the electricity market liberalization process in Singapore and the Philippines on these expected outcomes during the period 2015–2020. The regression analysis results suggest that in the specified period, the liberalization of the electricity market in Singapore has delivered both household and industry electricity price reductions and improvement in the RE share. However, there is no significant effect of the electricity market liberalization process on the electricity generated CO2 emissions. For the same period, the results imply that with the electricity market liberalization process in the Philippines, the electricity prices for household consumers and electricity-generated CO2 emissions have increased. Additionally, the liberalization process has no significant impact on both the RE share and industry electricity prices in the Philippines. To overcome the obstacles and strike a balance between the expected outcomes, policy recommendations are given for ASEAN economies following the pathway of liberalized electricity markets.

Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence

An accurate power load prediction in smart grid plays an important role in maintaining the balance between power supply and demand and thus ensuring the safe and stable operation of power system. In this paper we develop a hybrid power load prediction method, which involves three main steps: data decomposition with the empirical mode decomposition method, data processes with the minimal redundancy maximal relevance method and the weighted gray relationship projection algorithm, and support vector machine prediction, whose parameters are optimized through the particle swarm optimization algorithm with a second-order oscillation and repulsive force factor. Moreover, we predict the power load with our hybrid forecasting method based on the real dataset from the electricity market in Singapore, and also compare our prediction results with those by using other forecasting methods. Our comparison results show that our novel hybrid method possesses a high accuracy in both the level and directional predictions.

Day-ahead risk averse market clearing considering demand response with data-driven load uncertainty representation: A Singapore electricity market study

Demand response program is being implemented in the National Electricity Market of Singapore, which boosts the flexibility of demand side to actively participate in the real-time electricity market. Meanwhile, it is also significant to implement such a program in the day-ahead market, since generation companies could arrange their generating plans and load providers are able to adjust their hourly purchasing schedules. However, uncertain factors should be considered in the demand response program of the day-ahead market, such as the uncertain electricity load. Regarding the issue, this paper proposes a day-ahead bidding and clearing framework considering demand response with uncertain and correlated nature of electricity loads. To this end, a data-driven Dirichlet process mixture model is introduced to represent the load uncertainty, which might bring about the economic risk. To further reduce such a risk, a worst-case conditional value at risk is integrated into our proposed framework, and a WCVaR based two-step risk averse market clearing model is proposed. Finally, we conduct numerical studies based on the Singapore electricity market. Numerical studies demonstrate the outperformance of Dirichlet process mixture model for the load uncertain representation, and also verify that the worst-case conditional value at risk based market clearing model could effectively reduce the economic risk while maximizing the social welfare.

The enlightenment of foreign power market monitoring index system design to China’s power market

China is currently in the process of power market reform. With the continuous deepening of power reform, there will be more and more uncertain factors in the future power market. Although the advancement of marketization will bring benefits to market players, it will also Bringing huge risks. Thus, a power market monitoring indicator system is of great significance to achieve market risk management. The indicator system needs to fully consider the particularity of power commodities. In the design process, the power market indicator system should be designed in accordance with local conditions and local conditions. This article first studies the risks faced by the power generation side, power sales companies and user side transaction entities in the power market environment. And expounding the reasons for these risks, and proposes countermeasures to adapt to the power market transaction risks. And then analyzing and summing up the United States, Nordic, and Singapore electricity market monitoring indicator system design. Finally putting forward reasonable suggestions for the design of China’s electricity market indicator system, which has an enlightening effect on the countermeasures of multiple transaction entities in China’s electricity market.

Demand-side flexibility and demand-side bidding for flexible loads in air-conditioned buildings

Demand-side flexibility (DSF) has been touted as a possible solution to the challenges in power system operation arising from increasing intermittent renewables penetration and the emergence of electric vehicles. In Singapore, where around 24 to 60% of electricity demand in buildings could be attributed to heating, ventilation, and air conditioning (HVAC) purposes, air-conditioned buildings represent a significant flexibility resource which could be used to provide DSF and help tackle these challenges. This study aims to investigate the DSF potential of Singapore’s building stock and to explore how this potential could be realized through demand-side bidding. To this end, a building energy modeling tool with explicit modeling of the relationship between occupant comfort and HVAC load with model predictive control, CoBMo, is used. CoBMo allows optimal load scheduling to be expressed as a linear programming problem: minimizing overall electricity cost while maintaining occupant comfort. A price-based market clearing model is developed to evaluate demand-side bidding implementation, for which a case study on Singapore’s Downtown Core district is developed. Three scenarios with possible future utility-scale photovoltaic (PV) penetration in Singapore’s electricity system are explored, alongside a sensitivity analysis and a comparison between centralized dispatch and demand-side bidding with price-quantity pairs and linear curves. Results of the analysis show that DSF potential varies between building types, depending on cooling load and occupancy schedule. When extreme price fluctuations happen in future Singapore electricity market with 10 GWp PV penetration, demand-side bidding could aid consumers to utilize their DSF potential by encouraging more effective energy use and in turn, reducing their total electricity cost.

Generation Revenue Regulation and Incentive Contract Design in Electricity Market

In order to curb market power, encourage investment and redistribute welfare, revenue regulation should be carried out in electricity market. The incentive contract represented by the Contract for Difference is a kind of regulation. This paper proposes a Government Authorized Contract based on Revenue Estimation Method (REM) from the perspective of regulating generation revenue. First, the principle and design of the Vesting Contract in Singapore electricity market is employed, analyzing its limitations in regulation. Then, the incentive contract based on REM is presented in four steps. Finally, the IEEE30 node classic system is adopted to demonstrate the proposed model, showing that it can promote generators to bid rationally.

Day-Ahead Risk Averse Market Clearing Considering Demand Response with Data-Driven Load Uncertainty Representation: A Singapore Electricity Market Study

Day-Ahead Risk Averse Market Clearing Considering Demand Response with Data-Driven Load Uncertainty Representation: A Singapore Electricity Market Study

Multimodal Feature Extraction and Fusion Deep Neural Networks for Short-Term Load Forecasting

Precise and reliable forecasting of short-term electricity load is essential to the development of smart grids. Particularly, deep neural networks (DNNs) are widely utilized for the prediction of short-term electricity load due to their automatic feature extraction ability. However, these available stacked deep-learning models may lose some temporal features or spatial features of original input data. To capture more comprehensive information, in this article, we present an integration scheme based on empirical mode decomposition (EMD), similar day methods, and DNNs to perform short-term load forecasting. It is especially worth noting that the electricity price is also an important factor for load variation, which is considered in our proposed scheme. Specifically, there are two primary layers: a feature extraction layer and a forecasting layer. In the feature extraction layer, EMD is applied to decompose load time series into several components, which are arranged into the 2-D input matrix of the convolutional neural network (CNN). Both the output vectors of the CNN and the raw load sequences are fed into the long short-term memory (LSTM) layer. Therefore, the whole EMD based CNN-LSTM approach extracts multimodal spatial-temporal features from input data. Meanwhile, the electricity price data is utilized to obtain multimodal spatial-temporal features in the same way. Additionally, the day and hour information and loads of similar days are to augment extra features for prediction. In the forecasting layer, the forecasting task is accomplished through a fully-connected neural network based on the outputs of the feature extraction layer. Leveraging these techniques enables our proposed scheme to extract more latent features, which significantly improve the accuracy. In order to demonstrate the performance of our proposed scheme, related experiments are conducted on actual data from the electricity market in Singapore. Compared to other available models, our proposed scheme is superior in graphic and numerical results.

Electricity market deregulation in Singapore – Initial assessment of wholesale prices

Singapore began a huge step to deregulate its electricity market since 2003, with the creation of the National Electricity Market of Singapore (NEMS) allowing for bid-ask offers to be made for the dispatch of electricity supply on the wholesale side. Subsequently, the retail market liberalised in tranches, with 80% of electricity consumers currently already given an option to select their electricity retailers since late 2014. This paper aims to quantitatively analyse how competitiveness in both the wholesale and retail market led to price decreases from 2014 to 2017, using daily data for electricity and oil prices. We find that supply competition and the more recent retail liberalisation efforts has possibly led to a combinatorial decrease in wholesale electricity prices by up to 9.11%, accounting for the influence of oil prices and volatility components. This work seeks also to bring some insights on what to expect from full retail contestability after the latter half of 2018.

Game-Theory based dynamic pricing strategies for demand side management in smart grids

With the increasing demand for electricity and the advent of smart grids, developed countries are establishing demand side management (DSM) techniques to influence consumption patterns. The use of dynamic pricing strategies has emerged as a powerful DSM tool to optimize the energy consumption pattern of consumers and simultaneously improve the overall efficacy of the energy market. The main objective of the dynamic pricing strategy is to encourage consumers to participate in peak load reduction and obtain respective incentives in return. In this work, a game theory based dynamic pricing strategy is evaluated for Singapore electricity market, with focus on the residential and commercial sector. The proposed pricing model is tested with five load and price datasets to spread across all possible scenarios including weekdays, weekends, public holidays and the highest/lowest demand in the year. Three pricing strategies are evaluated and compared, namely, the half-hourly Real-Time Pricing (RTP), Time-of-Use (TOU) Pricing and Day-Night (DN) Pricing. The results demonstrate that RTP maximizes peak load reduction for the residential sector and commercial sector by 10% and 5%, respectively. Moreover, the profits are increased by 15.5% and 18.7%, respectively, while total load reduction is minimized to ensure a realistic scenario.

Day-Ahead Price Forecasting in Asia's First Liberalized Electricity Market using Artificial Neural Networks

This paper proposes a comparative model for the day-ahead electricity price forecasting that could be realized using multi-layer neural network (MLNN) with levenberg-marquardt (LM) algorithm, generalized regression neural network (GRNN) and cascade-forward neural network (CFNN). In this work applications of various models were applied to national electricity market of Singapore (NEMS), i.e. Asia's first liberalized electricity market. The individual price of year 2006 is very volatile with a very wide range. Therefore, accurate forecasting models are required for Singapore electricity market company (EMC) to maximize their profits and for consumers to maximize their utilities. Hence the year 2006 has been taken for forecasting the uniform Singapore electricity price (USEP). The mean absolute percentage error (MAPE) results show that the proposed CFNN model possess better forecasting abilities than the other models and its performance was least affected by the volatility.

Electricity market structure, electricity price, and its volatility

The transition of the Singapore electricity market structure shows that real-time pricing with vesting contracts appears more effective than day-ahead or real-time pricing with a cap in maintaining a low electricity price and its volatility.

The New Electricity Market of Singapore: Regulatory framework, market power and competition

This study examines whether the New Electricity Market of Singapore (NEMS) is functioning at a workable level of competition. The generation market of the NEMS appears highly concentrated by a four-firm concentration ratio or the Herfindahl–Hirschman Index. However, other measures of market power present that the NEMS is working at close to a competitive market. First, there seems to be a number of effective competitors in the market. Second, Supply Margin Assessment and Residual Supply Index support that the market is competitive though there are some possibilities in which the largest generator or a few large generators jointly could still have market power. Third, the Lerner Index of the NEMS shows that the generation market is fairly competitive and the Lerner Index adjusted with an industry level price elasticity of demand implies that there has not been much exercise of market power. Finally, vesting contracts—a contractual obligation of a specified quantity of electricity supply to the market—have appeared to be a strong and effective tool to mitigate market power in the NEMS. The vesting contracts are considered the force behind the lowering in the average Uniform Singapore Electricity Price and the Lerner Index in 2004.

Efficiency and deregulation of the electricity market in Singapore

This study examines production efficiency of electricity generation in the New Electricity Market of Singapore (NEMS), where deregulation is currently proceeding. Singapore is reliant on foreign direct investments and exports so competition from countries with lower costs such as China and India is exerting pressure on the government to reduce the costs of doing business here. Electricity cost is one of these. Deregulation is believed to be able to bring about lower electricity costs due to the various efficiency gains possible. This study concerns itself mainly with production efficiency and attempts to calculate possible production efficiency gains by using linear programming model. Production-efficiency gains are quantified by the base case scenario of continued regulation versus four counterfactual deregulation scenarios. The results indicate that cost gains could be about eight per cent of current production cost, and this is possibly a lower-bound estimate. However, whether the purported efficiency gains are realized is to be seen as the deregulation proceeds.