A hybrid renewable energy system for a longhouse

Renewable energy resources have already become an important alternative electric power generation technologies, due to the negative impacts of global warming on the environment, brought about by the use of fossil-fuelled generation. To combat such impacts, a hybrid energy system which consists of more than one source of renewable energy would be a good alternative to replace conventional electricity generation for Malaysia’s rural areas. A longhouse, or ‘Rumah Panjang’ in the local language, is a timber house raised three to five feet off the ground on stilts. Between 20 – 40 families of the ‘Rungus’, an ethnic group in the Borneo, residing primarily in northern Sabah, in the area around Kudat, dwell these longhouses. Due to the limitation of electricity access in that area, a hybrid system that consists of solar and wind energies as well as energy storage is proposed as a standalone renewable energy system for electricity supply. In this paper, three load profiles, representing various weather conditions; including hot, rainy and normal weather days are developed to represent the annual load curve. Meteorological data of solar irradiation and wind speed are collected at the Kudat area. Modelling of the hybrid system is then carried out based on selecting the most suitable system components, such as PV arrays, wind turbines, batteries and the inverter that satisfy both the technical and financial feasibility criteria. The model is then simulated using HOMER software to calculate the net present cost and the levelized cost of energy (LCOE). Results of the hybrid system simulation are compared with a diesel power generation, representing conventional energy supply, as the existing energy source. The comparison highlights the economic viability of the proposed hybrid system as a sustainable energy alternative to supply electricity to the longhouse

Electrostatic effect due to patch potentials between closely spaced surfaces

The spatial variation and temporal variation in surface potential are important error sources in various precision experiments and deserved to be considered carefully. In the former case, the theoretical analysis shows that this effect depends on the surface potentials through their spatial autocorrelation functions. By making some modification to the quasi-local correlation model, we obtain a rigorous formula for the patch force, where the magnitude is proportional to ${\frac{1}{{{a}^{2}}}{{(\frac{a}{w})}^{\beta (a/w)+2}}}$ with ${a}$ the distance between two parallel plates, ${w}$ the mean patch size, and ${\beta}$ the scaling coefficient from ${-2}$ to ${-4}$. A torsion balance experiment is then conducted, and obtain a 0.4 mm effective patch size and 20 mV potential variance. In the latter case, we apply an adatom diffusion model to describe this mechanism and predicts a ${f^{-3/4}}$ frequency dependence above 0.01 ${\rm mHz}$. This prediction meets well with a typical experimental results. Finally, we apply these models to analyze the patch effect for two typical experiments. Our analysis will help to investigate the properties of surface potentials

An immediate-return reinforcement learning for the atypical Markov decision processes

The atypical Markov decision processes (MDPs) are decision-making for maximizing the immediate returns in only one state transition. Many complex dynamic problems can be regarded as the atypical MDPs, e.g., football trajectory control, approximations of the compound Poincaré maps, and parameter identification. However, existing deep reinforcement learning (RL) algorithms are designed to maximize long-term returns, causing a waste of computing resources when applied in the atypical MDPs. These existing algorithms are also limited by the estimation error of the value function, leading to a poor policy. To solve such limitations, this paper proposes an immediate-return algorithm for the atypical MDPs with continuous action space by designing an unbiased and low variance target Q-value and a simplified network framework. Then, two examples of atypical MDPs considering the uncertainty are presented to illustrate the performance of the proposed algorithm, i.e., passing the football to a moving player and chipping the football over the human wall. Compared with the existing deep RL algorithms, such as deep deterministic policy gradient and proximal policy optimization, the proposed algorithm shows significant advantages in learning efficiency, the effective rate of control, and computing resource usage

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