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Control of the superheated steam temperature is a significant technical challenge to coal-fired power plants due to the strong nonlinearity, large inertia and parameter uncertainties with external disturbances. This paper proposes a robust proportion–integration–differentiation (PID) control of the superheated steam temperature with proven stability and the external disturbance rejection. The design is based on the proposed data-driven feedback compensator (DFC) which is a neural network (NN) trained to isolate the nonlinearity and inertia from the PID controller, allowing the system for tuning the PID controller can be simplified as an approximate linear system with the modeling error based-feedback and feed-forward compensations. Then, the PID controller is tuned by the Nyquist stability criterion to place every closed-loop pole to a specific region in the left-half s-plane to guarantee the stability with considering the modeling error. Besides, a NN based feed-forward compensator is trained as the inverse model of the feedback compensator to further smooth the temperature fluctuation caused by the disturbance. The simulations and engineering implementation of the superheated steam temperature controls for a coal-fired power plant in Australia show the effectiveness. • A data-driven compensation based robust control with considering uncertainties. • A neural feedback compensator to compensate the nonlinearity and inertia. • Approximation errors handled by the robust PID control with proven stability. • The effectiveness verified by the implementation in a physical coal-fired plant. [ABSTRACT FROM AUTHOR]

Despite early calls to investigate the application of Vickrey auctions in electricity markets, academic literature still provides little insight into the matter. Similarly, despite general auction-theoretic literature discussing various advantages of employing revealed rather than sealed bidding procedures, publications on electricity market auctions rarely even mention differences between these opposing features. This paper marries these neglected features into a new auction model for electricity markets. We propose a novel open-bid, descending-clock auction capable of handling inter-bidder dependencies such as thermal line limits. A novel set of termination criteria ensures that our proposed auction facing markets with power balance and generator capacity constraints and thermal line limits, preserves truthful bidding as a dominant strategy, which, if employed by all bidders, allocates supply efficiently constituting an ex-post perfect equilibrium. Furthermore, we discuss known disadvantages of Vickrey–Clarke–Groves type auctions in the context of our proposed auction applied in electricity markets. • Proposition of a novel auction design considering electricity grid constraints. • Given quasi-linear utility, truthful bidding is weakly dominant utilising open bids. • Truthful bidding by all bidders results in efficient supply allocation. • Discussion of real-world electricity market design impacting auction properties. [ABSTRACT FROM AUTHOR]

The increasing share of renewable energy sources (RESs) in electricity generation leads to increased uncertainty of generation, frequency and voltage regulation as well as difficulties in energy management. A virtual power plant (VPP), as a combination of dispersed generator units, controllable load and energy storage system (ESS), provides an efficient solution for energy management and scheduling, so as to reduce the cost and network impact caused by the load spikes. This paper proposes a multi-objective optimization (MOO) of battery energy storage system (BESS) for VPP applications. A low-voltage (LV) network in Alice Springs (Northern Territory, Australia) is considered as the test network for this study. The BESS for each customer is used to store and release the energy when required to maintain the voltage regulation performance of the LV network and reduce the cost. To optimize the charge/discharge schedule in each battery, a multi-objective optimization tool (MOOT) is developed, where MOO can directly communicate with DIgSILENT PowerFactory platform to perform multi-time scale simulation. MOO mainly focuses on generating a set of trade-off schedule solutions for each customer over 24 h by considering the customer's cost and network impact. Then according to the stakeholder's prior preference, one of the solutions is selected and verified in DIgSILENT PowerFactory with a realistic LV network. We also design several scenarios with different penetrations of photovoltaics (PVs) and batteries to verify how they influence customer's cost and LV network. With the increasing penetrations of PVs and batteries, the experimental results over the selected solution according to the customer preference show that the customers' cost can be largely reduced by maintaining the network voltage regulation performance. • A generalized VPP BESS optimization framework is proposed. • Optimization of BESS in a VPP setting is modeled as an MOO problem. • MOOT facilitates the two-way communication between MOO and DIgSILENT PowerFactory directly without manual operations. • Large PV and battery penetration can largely reduce the customers' cost while maintaining the voltage level. [ABSTRACT FROM AUTHOR]

Highlights • Optimization model developed for PV–battery systems in P2P energy trading market. • P2P energy trading implements real-world constraints and market signals. • Analysis shows household energy savings sensitive to multiple parameters. • Maximal savings up to 28% achieved when equipped with large PV-battery on weekdays. • Battery-only households achieve fewer savings compared to those without any DG. Abstract Distributed generation (DG) based on rooftop photovoltaic (PV) systems with battery storages is a promising alternative energy generation technology to reduce global greenhouse gas emissions. As regulatory tariff-based incentives are diminishing, innovative solutions are required to sustain this renewable energy generation. An optimization model is proposed to maximize the economic benefits for rooftop PV-battery DG in a peer-to-peer (P2P) energy trading environment. The goal of the proposed model is to investigate the feasibility of such renewable source participated P2P energy trading by examining the economic benefits. The model is illustrated in a simulation framework for a local community with 500 households under real-world constraints encompassing PV systems, battery storage, customer demand profiles and market signals including the retail price, feed-in tariff and P2P energy trading mechanism. Interactions among peer-to-peer trading stakeholders are examined, quantifying household savings for different scenarios of this P2P-based DG. Household energy savings are identified to be sensitive to many factors including the scale of PV systems, the PV penetration, the P2P trading margins, the presence of battery storage and energy trading time. The model shows that maximal savings up to 28% can be achieved by households equipped with larger PV systems and battery storages during weekdays from an exemplified case. The sensitivity analysis demonstrates that households with PV systems have lower savings when PV penetration is high owing to excessive energy traded on the P2P market, pushing down the clearing price and the savings gain. Households with a battery-only configuration are shown to achieve fewer savings in contrast to households without any renewable resources in a P2P trading community. The model’s energy insights are the beginning of understanding the actual impact of policy, market and technical signals on economic benefits for household distributed renewable generation in a P2P energy trading market. [ABSTRACT FROM AUTHOR]