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This paper proposes a market mechanism for multi-interval electricity markets with generator and storage participants. Drawing ideas from supply function bidding, we introduce a novel bid structure for storage participation that allows storage units to communicate their cost to the market using energy-cycling functions that map prices to cycle depths. The resulting market-clearing process – implemented via convex programming – yields corresponding schedules and payments based on traditional energy prices for power supply and per-cycle prices for storage utilization. We illustrate the benefits of our solution by comparing the competitive equilibrium – assuming price-taker participants – of the resulting mechanism to that of an alternative solution that uses prosumer-based bids. Our solution shows several advantages over the standard prosumer-based approach that prices energy per slot. It does not require a priori price estimation. It also incentivizes participants to reveal their truthful cost, thus leading to an efficient, competitive equilibrium. Numerical experiments using New York Independent System Operator (NYISO) data validate our findings. • Study the effect of storage bid structure on electricity market efficiency. • Consider a competitive equilibria analysis with price-taker participants. • Storage bids based on pricing energy per slot lead to inefficiencies. • Propose a novel market mechanism for storage that relies on pricing energy cycles. • The cycle-based mechanism aligns with the social optimum. [ABSTRACT FROM AUTHOR]

Centralized planning of the electricity network, natural gas network and energy hubs (EHs) implicitly assumes a vertically integrated structure, which ignores the common independent ownership of different systems. A more practical approach should differentiate the electric system, gas system and each EH as separate stakeholders, and establish a distributed planning framework. Such a distributed planning framework based on the alternating direction method of multipliers (ADMM) is proposed in this paper, which uses the amount of electricity and natural gas required by EHs from each node as the decoupling information, and decomposes the joint planning problem into multiple planning sub-problems, respectively for the electric system, natural gas system and each EH. To reflect the operation more accurately in the planning stage, unit commitment is embedded in the planning model as its operation module. Finally, the proposed method is verified on an illustrative system composed of a modified IEEE RTS 24-bus electric system, Belgian 20-node natural gas system and four EHs. The case study demonstrates the impacts of unit commitment, gas price and penalty parameters on the planning scheme and the number of iterations. • An (n + 2) -agent distributed planning for one electric system, one natural gas system, and n energy hubs. • Distributed siting of energy hubs alongside planning of electric transmission lines and gas pipelines. • Unit commitment embedded in the planning framework to enhance operational flexibility. • Distributed planning with ADMM approaching the performance of the centralized one. [ABSTRACT FROM AUTHOR]

The climate change is addressing unprecedented pressures on conventional power system regarding the significant fossil fuel consumptions and carbon emissions, which largely challenges the conventional power system operation. This paper proposes a novel dynamic non-dominated sorting multi-objective biogeography-based optimization (Dy-NSBBO) to solve multi-objective dynamic economic emission load dispatch considering the mass integration of plug-in electric vehicles (PEVs), namely MO-DEELDP problem. First, a real-world economic emission load dispatch considering PEVs charging is first formulated as a constrained dynamic multi-objective optimization problem. Then a new multi-objective BBO is proposed adopting the non-dominated solution sorting technique, change detection and memory-based selection strategies in the multi-objective BBO method to strengthen the dynamic optimization performance. The proposed Dy-NSBBO is applied to solve three different dynamic economic emission load dispatch cases integrating four plug-in electric vehicle charging scenarios respectively. Comprehensive analysis shows that the novel algorithm is promising to bring considerable economic and environmental benefits to the power system operators and provides competitive charging strategies for policy makers and PEVs aggregators. [ABSTRACT FROM AUTHOR]