Triggered by the increased fluctuations of renewable energy sources, the European Commission stated the need for integrated short-term energy markets (e.g., intraday), and recognized the facilitating role that local energy communities could play. In particular, microgrids and energy communities are expected to play a crucial part in guaranteeing the balance between generation and consumption on a local level. Local energy markets empower small players and provide a stepping stone towards fully transactive energy systems. In this paper we evaluate such a fully integrated transactive system by (1) modelling the energy resource management problem of a microgrid under uncertainty considering flexible loads and market participation (solved via two-stage stochastic programming), (2) modelling a wholesale market and a local market, and (3) coupling these elements into an integrated transactive energy simulation. Results under a realistic case study (varying prices and competitiveness of local markets) show the effectiveness of the transactive system resulting in a reduction of up to 75% of the expected costs when local markets and flexibility are considered. This illustrates how local markets can facilitate the trade of energy, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition.Index Terms-Demand response, local electricity markets, microgrids, transactive energy, smart grids, stochastic optimization. NOTATIONIndices: e energy storage systems (ESSs) i distributed generation (DG) units l, m, s, t, v loads, markets, scenarios, periods, electric vehicles (EVs) Sets and subsets: Ω DG , Ω load set of DG units/loads Ω d DG ,Ω nd DG subset of dispatchable/non-dispatchable DG units Ω curt load ,Ω inte load subset of curtailable/interruptible loads Ω shift load subset of shiftable loads Parameters: C DG generation cost of DG unit (m.u./kWh) C ESS − ,C EV − discharging cost of ESS/EV (m.u./kWh) Ccurt,C inte ,C shift load curtailment/interruption/shift cost (m.u./kWh) C imb grid imbalance cost (m.u./kWh) M P electricity market price (m.u./kWh) Ne, N i , N l number of ESS/DG/loads Nm, Ns, Nv number of markets/scenarios/EVs Pcurt max maximum load reduction of Ω curt load (kW) P DG max/min maximum/minimum power of dispatchable DGs (kW) P DG nd forecast power of non-dispatchable DGs (kW) P ESS/EV + max maximum charge rate of ESSs/EV (kW) P ESS/EV − max maximum discharge rate of ESSs/EV (kW) P ESS max/min maximum/minimum energy capacity of ESSs (kWh) P EV max/min maximum/minimum energy capacity of EVs (kWh) P EV trip forecasted energy demand for EVs' trip (kWh) P load forecasted active power of loads (kW) P offer max/min maximum/minimum energy offer in markets (kW) P shift forecasted power of Ω shift load in T shift (kW) P shift max maximum load shifted of Ω shift load in T shift (kW) T number of periods T shift shift interval of Ω shift load T start shift /T end shift earliest/latest possible period for load shift of Ω shift load η EV + /EV − charging/discharging efficiency of EV...

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a b s t r a c tThe electricity market sector has suffered massive changes in the last few decades. The worldwide electricity market restructuring has been conducted to potentiate the increase in competitiveness and thus decrease electricity prices. However, the complexity in this sector has grown significantly as well, with the emergence of several new types of players, interacting in a constantly changing environment. Several electricity market simulators have been introduced in recent years with the purpose of supporting operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents a new, enhanced version of MASCEM (Multi-Agent System for Competitive Electricity Markets), an electricity market simulator with over ten years of existence, which had to be restructured in order to be able to face the highly demanding requirements that the decision support in this field requires. This restructuring optimizes the performance of MASCEM, both in results and execution time.

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multiagent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) -a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

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Spectroscopic data of dye decolorizing peroxidases (DyPs) from Bacillus subtilis (BsDyP), an A subfamily member, and Pseudomonas putida (PpDyP), a B subfamily enzyme, reveal distinct heme coordination patterns of the respective active sites. In solution, both enzymes show a heterogeneous spin population, with the six-coordinated low-spin state being the most populated in the former and the five-coordinated quantum mechanically mixed-spin state in the latter. We ascribe the poor catalytic activity of BsDyP to the presence of a catalytically incompetent six-coordinated low-spin population. The spin populations of the two DyPs are sensitively dependent on the pH, temperature, and physical, i.e., solution versus crystal versus immobilized, state of the enzymes. We observe a redox potential for the Fe(2+)/Fe(3+) couple in BsDyP (-40 mV) at pH 7.6 substantially more positive than those reported for the majority of other peroxidases, including PpDyP (-260 mV). Furthermore, we evaluate the potential of the studied enzymes for biotechnological applications on the basis of electrochemical and spectroelectrochemical data.

The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources' particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator-MIBEL.

Power systems are showing a dynamic evolution in the last few years, caused in part by the adoption of smart grid technologies. The integration of new elements that represent a source of uncertainty, such as renewables generation, electric vehicles, variable loads and electricity markets, poses a higher degree of complexity causing that traditional mathematical formulations struggle in finding efficient solutions to problems in the smart grid context. In some situations, where traditional approaches fail, computational intelligence has demonstrated being a very powerful tool for solving optimization problems. In this paper, we analyze the application of Differential Evolution (DE) to address an energy resource management problem under uncertain environments. We perform a systematic parameter tuning to determine the best set of parameters of four stateof-the-art DE strategies. Having knowledge of the sensitivity of DE to the parameter selection, self-adaptive parameter control DE algorithms are also implemented, showing that competitive results can be achieved without the application of parameter tuning methodologies. Finally, a new hybrid-adaptive DE algorithm, HyDE, which uses a new "DE/target − to − perturbed best/1" strategy and an adaptive control parameter mechanism, is proposed to solve the problem. Results show that DE strategies with fixed parameters, despite very sensitive to the setting, can find better solutions than some adaptive DE versions. Overall, our HyDE algorithm excelled all the other tested algorithms, proving its effectiveness solving a smart grid application under uncertainty.