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Abstract This research is conducted to determine the limiting values of the geometric concentration when used with solar thermal system (thermoelectric generator) (TEG) to maintain desired hot and cold side temperatures for power generation. Experiments were conducted to determine the optimum solar concentration (aperture area/target area) using a thermoelectric generator sandwiched between the target plate and passive heat sink. A computer model is developed to solve the energy balance equations and find the optimum values for geometric concentration. It was observed that for the single configuration of heat sink and thermoelectric generator in a system, the trend of temperature difference between the hot and cold sides remain the same at different geometric concentrations. The optimum geometric concentration is determined for heat sinks in study. It is observed that with solar radiation intensity of 800 W/m2 and heat sink fin length of 0.15m the optimum geometric concentration is 13.

Abstract To achieve optimal generation from a number of mixed power plants by minimizing the operational cost while meeting the electricity demand is a challenging optimization problem. When the system involves uncertain renewable energy, the problem has become harder with its operated generators may suffer a technical problem of ramp-rate violations during the periodic implementation in subsequent days. In this paper, a scenario-based dynamic economic dispatch model is proposed for periodically implementing its resources on successive days with uncertain wind speed and load demand. A set of scenarios is generated based on realistic data to characterize the random nature of load demand and wind forecast errors. In order to solve the uncertain dispatch problems, a self-adaptive differential evolution and real-coded genetic algorithm with a new heuristic are proposed. The heuristic is used to enhance the convergence rate by ensuring feasible load allocations for a given hour under the uncertain behavior of wind speed and load demand. The proposed frameworks are successfully applied to two deterministic and uncertain DED benchmarks, and their simulation results are compared with each other and state-of-the-art algorithms which reveal that the proposed method has merit in terms of solution quality and reliability.

In underground mining, it is not currently feasible to forecast a coal burst incident. A coal burst usually includes suddenly abrupt energy release in line with the significant deformed shape in a coal mass as well as coal ejection. The major source of the released energy is the energy stored in the coal. The effect of geological characteristics in the coal on the possible released energy due to material and joint damping is classified as a current silent issue. Therefore, innovative research is needed to understand the influence of coal’s joint and cleat characters (directions and densities) on the possible energy release and/or dissipation. A simple and novel analytical solution is developed in this paper to calculate the amount of released energy due to varying joint density. A broad validation is conducted by comparing the outcomes of the developed analytical model with the results of a three-dimensional numerical simulation using the commercial discrete element package 3DEC. An appropriate agreement has been observed between the results from the numerical modelling and the suggested closed form solution. The paper derives a novel analytical solution to calculate the amount of released energy in coal with different joint densities.

Large-scale compressed air energy storage (CAES) is conventionally used in power systems. However, application of CAESs at the distribution level is limited because of differences in design and efficiency. On the other hand, application of electrical batteries suited for distribution networks (DNs) faces also challenges from high investment cost and significant degradation. In this regard, this paper presents the participation of an active distribution system equipped with a small-scale CAES (SCAES) in the day-ahead wholesale market. To make CAES applicable to DNs, thermal-electrical setting design of the SCAES coupled with a packed-bed heat exchanger is adopted in the operation of the grid, where SCAES performs as an energy storage for DNs to surpass existing deficiencies of battery banks. The electrical/thermal conversion rate has been modeled for the SCAES operation. Moreover, the operation strategy of the SCAES is optimally coordinated with an electric vehicle charging station (EVCS) as an alternative energy storage technology in deregulated DNs. To make EVCS simulation more realistic, Gaussian Copula probability distribution function is used to model the behavior of the EVCS. The results obtained from different case studies confirm the value of SCAES as a reliable energy storage technology for DNs.

Much of New South Wales and southern Queensland suffered from extreme drought from 2017 to 2019. This study models drought and bushfires impacts using VU‐TERM, a multi‐regional, dynamic CGE model. Prolonged drought pushed national real GDP to 0.7 per cent or more below base in 2018–2019 and 2019–2020. NSW’s real GDP fell relative to forecast by 1.1 per cent or $6.9 billion in 2018–2019 and 1.6 per cent or $10.2 billion in 2019–2020. These impacts reflect a severe diminution of farm output, given that agriculture accounts for around 1.6 per cent of NSW’s income. Bushfires exacerbated 2019–2020 losses. We assume that there is a full recovery in seasonal conditions in 2020. However, prolonged drought and bushfire destruction deplete farm capital through depressed investment and diminished herd numbers. Consequently, the income earning capacity of farms in recovery remains below that of a no drought base. The net present value of the national welfare loss is $63 billion, split between $53 billion in losses from drought and $10 billion from bushfires. The latter excludes any valuation of human lives lost, flora, fauna or forestry destruction. In the longer term, adaptation and policy responses will need to reflect the expectation of increased frequency of adverse climatic events.

This paper investigates the impacts of installing regulated wind and electricity storage on average price and price volatility in electricity markets. A stochastic bi-level optimization model is developed, which computes the optimal allocation of new wind and battery capacities, by minimizing a weighted sum of the average market price and price volatility. A fixed budget is allocated on wind and battery capacities in the upper-level problem. The operation of strategic/regulated generation, storage, and transmission players is simulated in the lower-level problem using a stochastic (Bayesian) Cournot-based game model. Australia's national electricity market, which is experiencing occasional price peaks, is considered as the case study. Our simulation results quantitatively illustrate that the regulated wind is more efficient than storage in reducing the average price, while the regulated storage more effectively reduces the price volatility. According to our numerical results, the storage-only solution reduces the average price at most by 9.4%, and the wind-only solution reduces the square root of price volatility at most by 39.3%. However, an optimal mixture of wind and storage can reduce the mean price by 17.6% and the square root of price volatility by 48.1%. It also increases the consumer surplus by 1.52%. Moreover, the optimal mixture of wind and storage is a profitable solution unlike the storage-only solution.

Abstract Global primary energy consumption will continue to increase with a high rate to 2050, which will be a big challenge for countries to meet both global and regional energy demand. Pumped storage stations (PSS) integrated to a hybrid power system (HPS) with solar and wind power for China are under construction to tussle with this challenge. Historically, modeling of a PSS integrated HPS has been ignored the interaction effect between the shaft vibration and the governing strategies, which will increase the dynamic risk of PSS disconnected immediately to HPS. Here we unify the models of the hydro-turbine governing system and hydro-turbine generator units with a novel expression of hydraulic forces. We quantize all the parameter’s interaction contributions of PSS integration to HPS and validate this model with the existing models. Finally, we show the feasibility of PSS’s model in integrating of a HPS under steady and fault scenarios.