Cost and Emission Optimization in Power Plant Using the Single Echelon Economic Dispatch Model (SEED)

A non-linear convex cost model for economic dispatch in microgrids

Conventional dispatch models, which are primarily focused on cost minimization, prove insufficient to address the multidimensional challenges of a Just Energy Transition. In order to address this discrepancy, the present paper puts forth the Economic, Environmental, and Social Dispatch (EEDS) model. The EEDS model is a Mixed-Integer Linear Programming (MILP) Unit Commitment formulation that explicitly incorporates socio-environmental externalities. The methodology implements a two-stage rolling horizon simulator (Day-Ahead and Real-Time) with high temporal resolution (5 min), validated on a polygeneration microgrid integrated with Battery Energy Storage Systems (BESS). The numerical results indicate that the incorporation of quantified social costs substantially modifies the merit order, effectively displacing technologies that are deemed to be socially regressive. Moreover, the analysis demonstrates that demand morphology is a pivotal factor in determining system performance, achieving zero Unserved Energy (ENS) and competitive prices across diverse profiles. Finally, the application of scenario analysis demonstrates that BESS is essential for managing diverse demand morphologies and moderating price volatility across different operational contexts. Therefore, the EEDS framework provides a rigorous quantitative foundation upon which economic efficiency, sustainability, and operational social justice can be balanced.

This paper contains a number of interesting ideas and we are grateful for the opportunity to comment on it. It is particularly pleasing to see that the authors have included a comparison of the forecasting methods under a realistic power system economic dispatch model. As the authors discuss, the loss function associated with the economic dispatch model is asymmetric, in that over-forecasting the realized wind power production requires deployment of additional reserve capacity, while the consequences of under-forecasting are minimal for the overall power system. The rotating regime-switching space-time diurnal (RRSTD) model and the autoregressive reference model are both inherently probabilistic in nature and can return full predictive distributions. Given the asymmetry of the loss function, it is likely that the optimal point forecast derived from a predictive distribution diverges from the center of the distribution (Gneiting, 2011). The authors partially account for this by employing the over-forecasting errors only in the reserve requirement component of the economic dispatch model. Furthermore, an inflation factor of 1.2 is used as a reliability margin. While this solution appears to work well in practice, we wonder if an additional economic gain could be achieved by fully utilizing the probabilistic properties of the RRSTD model in conjunction with the statistical properties of the loss function under the economic dispatch model. The RRSTD method predicts the wind speed at the turbine location, which is then subsequently transformed to wind power using the power curve provided by the turbine manufacturer. In a recent paper, Messner et al. (2013) apply wind speed

In order to coordinate the impact of the integration of electric vehicles and wind power on the economic dispatch of power systems, a multi-objective dispatch model for the environmental economy of wind farms with PHEVs connected to the grid was constructed. The uncertain factors of wind power generation and load will increase the difficulty of dispatching. The fuzzy theory will be used to realize the fuzzy modelling of the coordinated connection of wind power and electric vehicles. The satisfaction index will be used to transform the multi-objective problems of deterministic environment and economy into single For the objective optimization problem, based on the improvement of the traditional particle swarm optimization algorithm using linear descent search ideas and the particle information sharing method considering boundary constraints, the improved PSO algorithm is used to solve the proposed dynamic environment and economic dispatch model. From the grid operation and vehicle owners from the perspective of demand, the constraints of the system’s rotation reserve and the daily travel of the owner are considered. The comparison and analysis of the simulation of algorithms and calculation examples and multiple scheduling schemes in different scenarios verify the rationality of the model and the superiority of the improved PSO algorithm for solving such dynamic scheduling problems.

In this paper, a computational framework for integrating wind power uncertainty and carbon tax in economic dispatch (ED) model is developed. The probability of stochastic wind power based on nonlinear wind power curve and Weibull distribution is included in the model. In order to solve the revised dispatch strategy, quantum-inspired particle swarm optimization (QPSO) is also adopted, which shows stronger search ability and quicker convergence speed. The dispatch model is tested on a modified IEEE benchmark system involving six thermal units and two wind farms using the real wind speed data obtained from two meteorological stations in Australia.

DRL based low carbon economic dispatch by considering power transmission safety limitations in internet of energy

Solving the correlated uncertain power injections of wind power generations and photovoltaic power generations is a tough challenge for economic dispatch in active distribution grids. Although the chance-constrained method can solve this problem, it takes more time to determine the chance constraints. Therefore, a cumulant-based optimal method is proposed to solve the chance-constrained model for the economic dispatch problem with correlated uncertain power injections. Firstly, we set power injection adjustments as variables and established the economic dispatch model. Secondly, based on the Cholesky decomposition and the Cornish-Fisher series expansion, we calculate the cumulants of correlated uncertain power injections and establish the explicit expressions of chance constraints in power injection spaces. Thirdly, combined with the cumulants, a heuristic algorithm is used to solve the economic dispatch model. Optimization results for the modified PG&E69-bus distribution grid are given to verify the effectiveness of the proposed method.

Hydrogen as an energy carrier represents one of the most promising carbon-free energy solutions. The ongoing development of power-to-gas (PtG) technologies that supports large-scale utilization of hydrogen is therefore expected to support hydrogen economy with a final breakthrough. In this paper, the economic performance of a MW-sized hydrogen system, i.e. a composition of water electrolysis, hydrogen storage, and fuel cell combined heat and power plant (FCCHP), is assessed as an example of hydrogen-based bidirectional electrical energy storage (EES). The analysis is conducted in view of the Danish electricity spot market that has high price volatility due to its high share of wind power. An economic dispatch model is developed as a mixed-integer programming (MIP) problem to support the estimation of variable cost of such a system taking into account a good granularity of the technical details. Based on a projected technology improvement by 2020, sensitivity analysis is conducted to illustrate how much the hydrogen-based EES is sensitive to variations of the hydrogen price and the capacity of hydrogen storage.

Virtual power plant (VPP) amalgamates diverse distributed resources, thereby unlocking the full potential of distributed energy’s dispatch capabilities. Energy storage is an effective means to address the uncertainty of renewable energy and achieve energy complementarity. This study meticulously evaluates the finite nature of traditional energy within VPP, the unpredictability associated with renewable energy outputs, and the constraints faced by energy storage devices. It introduces strategic operational frameworks for VPP through cooperation between VPP operator and energy storage provider managing new type of electric energy storage systems. With the dual objectives of amplifying the economic gains for VPP operator and maximizing benefits for energy storage provider, this research formulates a VPP economic low-carbon dispatch model, guided by a carbon tax framework. The improved particle swarm optimization algorithm is used to solve the proposed dispatch model. The numerical results confirmed that the collaboration between the VPP operator and the energy storage provider mitigates the issues caused by the variability of renewable energy outputs on the VPP, resulting in a mutually beneficial scenario for both the operator and the storage provider. Additionally, carbon emissions have been drastically decreased in response to low carbon targets.

A new approach to secure economic power dispatch

In view of uncertainties caused by large-scale wind power integration, energy storage system (ESS) is being considered to stabilize the fluctuation of wind power. In this paper, the influence of ESS on power system operation with wind power is analyzed in detail, and an economic dispatch (ED) model with wind power and ESS is proposed based on scenario set. First, the initial scenario set of wind power output is generated by the Monte Carlo sampling. To overcome the shortcoming of heavy dependence on the initial clustering centers, which usually leads to unstable clustering results, the k-means clustering is improved by combining self-organizing feature map neural network and particle swarm optimization (PSO). Then, the initial scenario set is reduced based on this improved k-means clustering method. Finally, an ED model solved by PSO is used to minimize the comprehensive power generation cost based on the reduced scenario set. Taking IEEE-39 bus system as an example, the scenario-set-based ED model is implemented in this paper. The simulation results show that, when solving the ED problem with wind power and ESS, the proposed method considering scenario reduction makes not only the clustering index better, but also the results of ED more reasonable.

In this paper, a method for sizing the reactive power compensation in a non-interconnected island power system is presented and applied to determine the necessary inductive reactive power compensation for the autonomous power system of Rhodes Island, Greece. The Rhodes power system is often confronted with an excess of reactive power, as a result—inter alia—of underground high-voltage (HV) cable lines and distributed generation penetration. Reactive power compensation is typically a local issue in power systems, usually aiming at maintaining an acceptable voltage profile on specific transmission segments, e.g., long underground or submarine cables. In autonomous systems, however, where network lengths are relatively short, reactive power compensation is meant to address the overall reactive power equilibrium of the system. The proposed method follows a three-step approach. First, power flow analysis is conducted to determine the size of the maximum compensation that may be necessary, i.e., the compensation size that practically allows unit commitment to be conducted without being constrained by reactive power considerations. Then, a unit commitment and economic dispatch model is executed over the course of a year to determine the optimal compensation size, using the output of the power flow analysis to formulate reactive power balance constraints. Finally, the results of the economic optimization are assessed in terms of dynamic security to verify the feasibility of the optimal solution.

A low‐carbon economic dispatch model for electricity market with wind power based on improved ant‐lion optimisation algorithm

This paper proposes a distributed robust dispatch approach to solve the economic dispatch problem of the interconnected systems with a high proportion of wind power penetration. First of all, the basic principle of synchronous alternating direction method of multipliers (SADMM) is introduced to solve the economic dispatch problem of the two interconnected regions. Next, the polyhedron set of the robust optimization method is utilized to describe the wind power output. To adjust the conservativeness of the polyhedron set, an adjustment factor of robust conservativeness is introduced. Subsequently, considering the operation characteristics of the DC tie line between the interconnected regions, an economic dispatch model with a high proportion of wind power penetration is established and parallel iteration based on SADMM is used to solve the model. In each iteration, the optimized power of DC tie lines is exchanged between the regions without requiring the participation of the superior dispatch center. Finally, the validity of the proposed model is verified by the examples of the 2-area 6-node interconnected system and the interconnection of several modified New England 39-node systems. The results show that the proposed model can meet the needs of the independent dispatch of regional power grids, effectively deal with the uncertainty of wind power output, and maximize the wind power consumption under the condition of ensuring the safe operation of the interconnected systems.

In today's world, with the rapid development of green and clean energy, the comparison of wind power in power network has been gradually improved. However, wind power has also brought new challenges to the stable operation of power system due to its inherent volatility and randomness. In this paper, the economic dispatching model of power system including wind power grid connection is studied. On the basis of stochastic expectation and chance-constrained programming, the specific economic dispatch model with wind power integration is given. The core of this paper is that we simulate the degree of wind power output fluctuation by setting different confidence levels. Simulation results show that a larger confidence interval can achieve better security, while a smaller confidence interval can achieve better economy.