From bidding strategy in smart grid toward integrated bidding strategy in smart multi-energy systems, an adaptive robust solution approach

Energy hub is a new concept in the field of energy systems. Using multiple energy carriers as their inputs, these systems are capable of supplying various kinds of energy demands which seems to be interesting for system operators in the future. Different renewable and non-renewable generation units can be incorporated in gas and electricity energy hub systems to provide sufficient energy for supplying different types of loads. Primary fuel consumed by the units inside hub system is usually natural gas which is procured from gas network. In addition to distributed generation units in the hub system, upper network is also available to provide a reliable power to the electrical load. As expressed above, different energy carriers are involved in the hub energy systems. So, utilization of energy storage system seems to be vital. One of the energy storage systems that can be integrated in the future hub energy systems is hydrogen energy storage system (HSS). In this chapter, performance of hub energy system has been investigated from economic and environmental viewpoints in the presence of hydrogen energy storage system and demand response program (DRP). Four case studies have been evaluated in a sample hub energy system and the results are analyzed for comparison.

The role of electricity market design for energy storage in cost-efficient decarbonization

Aiming at integrating different energy sectors and exploiting the synergies coming from the interaction of different energy carriers, sector coupling allows for a greater flexibility of the energy system, by increasing renewables’ penetration and reducing carbon emissions. At the local level, sector coupling fits well in the concept of an integrated local energy community (ILEC), where active consumers make common choices for satisfying their energy needs through the optimal management of a set of multi-carrier energy technologies, by achieving better economic and environmental benefits compared to the business-as-usual scenario. This paper discusses the stochastic operation optimization of the smart Savona Campus of the University of Genoa, according to economic and environmental criteria. The campus is treated as an ILEC with two electrically interconnected multi-energy hubs involving technologies such as PV, solar thermal, combined heat and power systems, electric and geothermal heat pumps, absorption chillers, electric and thermal storage. Under this prism, the ILEC can participate in the day-ahead market (DAM) with proper bidding strategies. To assess the renewables’ uncertainties, the roulette wheel method is used to generate an initial set of scenarios for solar irradiance, and the fast forward selection algorithm is then applied to preserve the most representative scenarios, while reducing the computational load of the next optimization phase. A stochastic optimization model is thus formulated through mixed-integer linear programming (MILP), with the aim to optimize the operation strategies of the various technologies in the ILEC, as well as the bidding strategies of the ILECs in the DAM, considering both energy costs and carbon emissions through a multi-objective approach. Case study results show how the optimal bidding strategies of the ILEC on the DAM allow minimizing of the users’ net daily cost, and, as in the case of environmental optimization, the ILEC operates in self-consumption mode. Moreover, in comparison to the current operation strategies, the optimized case allows reduction of the daily net energy cost in a range from 5 to 14%, and the net daily carbon emissions in a range from 6 to 18%.

Strategic bidding for electricity supply in a day-ahead energy market

Day-ahead and real-time market bidding and scheduling strategy for wind power participation based on shared energy storage

The role of energy storage and demand response as energy democracy policies in the energy productivity of hybrid hub system considering social inconvenience cost

Tri-objective optimal scheduling of smart energy hub system with schedulable loads

This paper investigates the formulation of a general optimal energy flow (OEF) problem for integrated energy systems, paying particular attention to economy issues, i.e. production, distribution and utilization of hydrogen, as well as considering the impact of energy storage devices. Based on the concept of energy hubs, the optimal conversion and transmission of multiple energy sources and energy carriers, in particular natural gas, electricity, district heat and hydrogen, considering energy storage devices are discussed. A 3 energy-hub system with electricity, gas, heat and hydrogen production, distribution, demand and storage capabilities is used to illustrate some of the proposed concepts and analysis techniques. The results illustrate some of the advantages of combining different energy sources and carriers, particularly if hydrogen is considered as an integral part of the energy system, given its storage characteristics.

Energy hub system (EHS) incorporating multiple energy carriers, storage, and renewables can efficiently coordinate various energy resources to optimally satisfy energy demand. However, the intermittency of renewable generation poses great challenges on optimal EHS operation. This article proposes an innovative distributionally robust optimization model to operate EHS with an energy storage system (ESS), considering the multimodal forecast errors of photovoltaic (PV) power. Both battery and heat storage are utilized to smooth PV output fluctuation and improve the energy efficiency of EHS. This article proposes a novel multimodal ambiguity set to capture the stochastic characteristics of PV multimodality. A two-stage scheme is adopted, where 1) the first stage optimizes EHS operation cost, and 2) the second stage implements real-time dispatch after the realization of PV output uncertainty. The aim is to overcome the conservatism of multimodal distribution uncertainties modeled by typical ambiguity sets and reduce the operation cost of EHS. The presented model is reformulated as a tractable semidefinite programming problem and solved by a constraint generation algorithm. Its performance is extensively compared with widely used normal and unimodal ambiguity sets. The results from this article justify the effectiveness and performance of the proposed method compared to conventional models, which can help EHS operators to economically consume energy and use ESS wisely through the optimal coordination of multienergy carriers.

This paper presents a two-stage adaptive robust optimization approach to develop an optimal bidding strategy for a grid-connected solar photovoltaic (PV) plant with a coupled energy storage system (ESS). This study models the power flow through system elements as well as the exact interactions between the system and upstream network. The uncertainties of solar radiation, affecting the PV generation and market prices are characterized by bounded intervals in polyhedral uncertainty sets. A robust optimization is formed as a min-max-min problem characterizing both “here-and-now” and “wait-and-see” variables. This tri-level robust optimization is solved through a decomposition approach, where it is recast into a min master problem and a max-min subproblem. Unlike previous conventional robust optimization models, that utilise duality for solving the inner subproblem, a block coordinate decent (BCD) methodology is used in this study. Accordingly, instead of conducting duality theory, the subproblem is solved over a first-order Taylor series approximation of uncertainties. This results in a moderate computation/mathematical burden. Moreover, there is no need to linearize the dualized problem anymore, as no duality is conducted. Using BCD methodology in solving the robust optimization model also allows modelling binary variables as recourse actions, which differentiates this approach to conventional dual-based robust optimization models. An illustrative example is provided to demonstrate the performance of the proposed bidding strategy model.

We propose a new approach for the optimal dayahead (DA) market bidding strategy of an aggregator of a power distribution system (with wind and solar generation). The proposed approach incorporates the stochasticity of renewable generation through a distributionally-robust chance-constraint (DRCC), which guarantees that the real-time (RT) energy shortfall (resulting from the DA market commitment and unexpected realization of renewable generation) does not exceed a pre-determined threshold with high probability, even without accurate information about the probability distribution of the random renewable generation. The formulated cost-minimization problem with DRCC is transformed into a deterministic, convex optimization problem. Numerical results demonstrate that the proposed approach enables the aggregator to efficiently trade-off profitability and risk, and that a properly chosen risk tolerance level (in the DRCC) can significantly reduce the average cost at DA market by 6–18.6% (compared with the robust solution), at the cost of negligible probability that the RT energy shortfall exceeds the pre-determined threshold.

Transmission congestion can lead to price separation at different buses in electricity markets. This may cause differences between the payment to the generation companies and the revenues collected from the loads, called merchandise surplus or congestion charge or congestion revenue. Through the Financial Transmission Right (FTR) auction, the Independent System Operator (ISO) redistributes the merchandise surplus to market participants (MPs). Although the FTR auction is conducted at a different time than the Day-ahead (DA) market, the two markets are related because the MP’s FTR position will be settled based on the DA market prices. For this reason, MPs may intend to develop a joint bidding strategy for FTR and DA markets in order to maximize its total profit in the two markets. This paper therefore proposes a bi-level optimization model in which the upper-level sub-problem formulates the profit maximization problem for a strategic generation company, and the lower-level sub-problem mimics a quasi ISO model. In this work, the strategic MP is considered to be price-taker in the FTR market and price-maker in the DA market, which allows the MP to influence the DA market price to benefit its combined portfolio value in two markets. To demonstrate the performance of the proposed model, it has been studied on a 6-bus test system. The results show that an optimal bidding strategy may choose to have a reduced profit in the DA market, while making a significantly higher profit in the FTR market so that the total profit is maximized.

We consider an energy management system that controls a cluster of price-responsive demands. Besides these demands, it also manages a wind-power plant and an energy storage facility. Demands, wind-power plant, and energy storage facility are interconnected within a small size electric energy system equipped with smart grid technology and constitute a virtual power plant that can strategically buy and sell energy in both the day-ahead and the real-time markets. To this end, we propose a two-stage procedure based on robust optimization. In the first stage, the bidding strategy in the day-ahead market is decided. In the second stage, and once the actual scheduling in the day-ahead market is known, we decide the bidding strategy in the real-time market for each hour of the day. We consider that the virtual power plant behaves as a price taker in these markets. Robust optimization is used to deal with uncertainties in wind-power production and market prices, which are represented through confidence bounds. Results of a realistic case study are provided to show the applicability of the proposed approach.

Robust bidding strategy of battery energy storage system (BESS) in joint active and reactive power of day-ahead and real-time markets

In this paper smart operation of micro energy hub is presented. Energy hub system consists of certain energy hubs and interconnectors that are coordinated by energy hub system operator. An energy hub contains several converters and storages to serve demanded services in most efficient manner from available energy carriers. In this paper, the flexibility of energy delivery point is enhanced using micro energy hub concept. Smart micro energy hub is operated in minimum cost considering the price of input energy carriers, the amount of forecasted output energy carriers and the available facilities included in the hub. Regarding this matter, two micro energy hubs with different characteristics are modeled which are equipped with CHP unit, warmer, boiler and heat storage. The effectiveness of the proposed system is validated by running numerical study on a test system.