Battery energy storage systems (BESS) are receiving great attention due to their ability in maximizing self-consumption and energy arbitrage. However, in many countries, BESS profitability is still questionable without subsidy due to their high capital costs. This paper proposes an open-source generic tool to provide comprehensive techno-economic analysis on the small-scale PV/BESS. The proposed tool utilizes real-time BESS control method that has been validated using real experimental measurements in addition to integrating a reliable degradation model to determine the loss in savings due to capacity degradation. The profitability is investigated by conducting different cost-benefit analyses for the PV and BESS. An optimization layer is introduced to find the optimal PV/BESS sizes that boost investment profitability by maximizing the net present value. A detailed case study is presented for a household in the U.K. under three different tariff structures that consider practical parameters and assumptions. Furthermore, sensitivity analyses are performed to evaluate the impact of the cost-benefit analysis assumptions on the PV/BESS sizing and profitability. Finally, the BESS investment viability is investigated over the next few years for different investment expectations to answer the question of when the BESS should be installed to be a viable economic-attractive option in the U.K.

Dissolved gas analysis (DGA) is a traditional approach for power transformer fault diagnostics based on measurement of gas contamination. Hydrocarbon gases generated and dissolved in transformer oil during operation can increase in density as fault conditions predominate. Critical determination of gas concentration changes and assessment trending of dissolved gases for fault prediction and prevention of transformer damage is essential. In this paper, a dynamic fault prediction approach is proposed using a long short-term memory (LSTM) model with intelligent classification to determine the running state of a transformer for prediction and avoidance of potential transformer damage. In the paper, the LSTM model processed DGA data collected from real on-site transformer field measurements and predicts future dissolved gas concentrations in time sequence. Four artificial intelligence diagnostic models (support vector machine (SVM), K-nearest neighbors (KNN), decision tree, and artificial neural network (ANN)) were rendered and used for comparative fault prediction assessment. By comparing experimental results from the different LSTM-based models, this paper asserts that the LSTM-KNN model provides the highest and most reliable prediction accuracy for power transformers.

Centralised, front-of-the-meter battery energy storage systems are an option to support and add flexibility to distribution networks with increasing distributed photovoltaic systems, which generate renewable energy locally and help decarbonise the power sector. However, the provision of specific services at distribution level remains under development for real applications in industry. To this end, this paper presents an exhaustive techno-economic analysis of the role of front-of-the-meter battery energy storage systems in primary distribution networks with presence of distributed PV covering: (i) the siting decision for storage systems using multi-objective genetic algorithm optimisation; (ii) the response when smart capabilities for PV inverters (e.g., volt-var control) are present; and (iii) the quantification of revenue streams and compensation schemes that would bring positive profitability when providing distribution-specific services based on the supply of real and reactive power. The performance of grid-level battery energy storage technology is evaluated in the IEEE 34-bus system particularised to the distribution code of Northern Ireland, UK. The techno-economic assessment covers one year of simulations at 1-minute resolution performed with the simulation tool EPRI OpenDSS and its Python communication interface. The results show that, besides the technical benefits of centralised battery storage systems, the economic compensation based on traditional transmission-level services is not sufficient for the profitability of these projects. Several ideas are explored to improve the profitability of these systems considering the local value and decarbonisation they add. The findings presented can direct system operators and regulators towards developing schemes to incentivise centralised battery energy storage projects in distribution networks in the context of distribution-level services.

The state and input constraints of nonlinear systems could greatly impede the realization of their optimal control when using reinforcement learning (RL)-based approaches since the commonly used quadratic utility functions cannot meet the requirements of solving constrained optimization problems. This article develops a novel optimal control approach for constrained discrete-time (DT) nonlinear systems based on safe RL. Specifically, a barrier function (BF) is introduced and incorporated with the value function to help transform a constrained optimization problem into an unconstrained one. Meanwhile, the minimum of such an optimization problem can be guaranteed to occur at the origin. Then a constrained policy iteration (PI) algorithm is developed to realize the optimal control of the nonlinear system and to enable the state and input constraints to be satisfied. The constrained optimal control policy and its corresponding value function are derived through the implementation of two neural networks (NNs). Performance analysis shows that the proposed control approach still retains the convergence and optimality properties of the traditional PI algorithm. Simulation results of three examples reveal its effectiveness.

The nutrition and flavor of shrimp paste came from hydrolyzation by enzymes that were produced by microorganisms. The salt‐tolerant strain Virgibacillus halodenitrificans ST‐1 isolated from shrimp paste was studied and used in the fermentation of shrimp paste. The strain and the protease produced by ST‐1 were investigated. The optimum pH of the protease was 8.0, and the reaction temperature was 30°C. The protease showed high activity in the range of pH (5.0–11.0) and NaCl concentration (1%–15%). Divalent cations such as Ba²⁺, Ca²⁺, Mg²⁺, Mn²⁺, and Si²⁺ could enhance the protease activity. Residual activity of protease was more than 90% when it was incubated with PMSF and H₂O₂. Also, the enzyme retained more than 90% of initial activity after it was incubated with organic solvents. Variety of natural proteins could be substrates of the protease. By analyzing the release rate of free amino acids, it was predicted that the cleavage sites of the protease were mainly Glu, Asp, Gly, Leu, and Lys. Moreover, the hydrolysates of the protease had antioxidant activity, especially for DPPH and superoxide anion radical scavenging. The strain ST‐1 and the protease both were excellent candidates for food industries.

Distribution system operators are attracted to battery energy storage systems (BESS) as a smart option to support the distribution network. However, due to its high capital cost, BESS profitability is dependent on the participation in multiple services to stack revenues and rationalize their existence. Yet, revenue stacking is location-dependent based on the available services and regulations. In this paper, specific revenue stacking frameworks are proposed for BESS installed in modern distribution networks that consider the conflicts and synergies that may occur from the involvement in multiple services in practice. A simple yet effective sizing formulation is introduced to find the BESS system size based on the primary service which is to solve the distribution network violations. BESS scheduling is simulated in accordance with the proposed frameworks to maximize the stackable profits for a case study of Northern Ireland. The BESS profitability is investigated through cost-benefit analyses of different technologies for the sole and stacked services. The results show that revenue stacking can boost the annual revenues by 129% with a payback period of 8 years on average. The presented insights are useful for network operators and energy investors in understanding and assessing the profitability of different BESS technologies for various applications.